{"id":10,"date":"2025-06-10T07:17:54","date_gmt":"2025-06-10T07:17:54","guid":{"rendered":"http:\/\/lsplr.iit.academiaromana-is.ro\/?page_id=10"},"modified":"2025-06-10T09:48:36","modified_gmt":"2025-06-10T09:48:36","slug":"fise-tehnologice","status":"publish","type":"page","link":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/fise-tehnologice\/","title":{"rendered":"Fi\u0219e tehnologice"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"10\" class=\"elementor elementor-10\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3f081879 e-flex e-con-boxed e-con e-parent\" data-id=\"3f081879\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2370c2fa e-n-tabs-mobile elementor-widget elementor-widget-n-tabs\" data-id=\"2370c2fa\" data-element_type=\"widget\" data-widget_type=\"nested-tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-tabs\" data-widget-number=\"594592506\" aria-label=\"Tabs. Open items with Enter or Space, close with Escape and navigate using the Arrow keys.\">\n\t\t\t<div class=\"e-n-tabs-heading\" role=\"tablist\">\n\t\t\t\t\t<button id=\"e-n-tab-title-5945925061\" class=\"e-n-tab-title\" aria-selected=\"true\" data-tab-index=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"e-n-tab-content-5945925061\" style=\"--n-tabs-title-order: 1;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tLDA\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925062\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925062\" style=\"--n-tabs-title-order: 2;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tSVM\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925063\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925063\" style=\"--n-tabs-title-order: 3;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tNLTK\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925064\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"4\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925064\" style=\"--n-tabs-title-order: 4;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tGraph networks\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925065\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"5\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925065\" style=\"--n-tabs-title-order: 5;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tNLP-Cube\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925066\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"6\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925066\" style=\"--n-tabs-title-order: 6;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tGloVe embeddings\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925067\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"7\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925067\" style=\"--n-tabs-title-order: 7;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tELMo embeddings\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925068\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"8\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925068\" style=\"--n-tabs-title-order: 8;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tCharacter-level embeddings\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-5945925069\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"9\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-5945925069\" style=\"--n-tabs-title-order: 9;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tTensorFlow\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250610\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"10\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250610\" style=\"--n-tabs-title-order: 10;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tSeq2seq\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250611\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"11\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250611\" style=\"--n-tabs-title-order: 11;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tLong Short-Term Memory\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250612\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"12\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250612\" style=\"--n-tabs-title-order: 12;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tTensorBoard\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250613\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"13\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250613\" style=\"--n-tabs-title-order: 13;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tWord2Vec\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250614\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"14\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250614\" style=\"--n-tabs-title-order: 14;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tPyMagnitude\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250615\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"15\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250615\" style=\"--n-tabs-title-order: 15;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tCTC\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250616\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"16\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250616\" style=\"--n-tabs-title-order: 16;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tRoBERT\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250617\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"17\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250617\" style=\"--n-tabs-title-order: 17;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tRNN encoder-decoders\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250618\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"18\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250618\" style=\"--n-tabs-title-order: 18;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tAten\u021bia (attention-based approaches)\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250619\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"19\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250619\" style=\"--n-tabs-title-order: 19;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tRoVG - Romanian Verbal Group Tagger\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250620\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"20\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250620\" style=\"--n-tabs-title-order: 20;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\teDTLR extraction software\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250621\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"21\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250621\" style=\"--n-tabs-title-order: 21;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tColec\u021bie de API-uri de acces \u00een RoWordNet (RoWN - Romanian WordNet)\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250622\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"22\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250622\" style=\"--n-tabs-title-order: 22;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tKeras\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250623\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"23\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250623\" style=\"--n-tabs-title-order: 23;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tYOLO\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250624\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"24\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250624\" style=\"--n-tabs-title-order: 24;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tWord embeddings\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-59459250625\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"25\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-59459250625\" style=\"--n-tabs-title-order: 25;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tCNN\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t\t<\/div>\n\t\t\t<div class=\"e-n-tabs-content\">\n\t\t\t\t<div id=\"e-n-tab-content-5945925061\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925061\" data-tab-index=\"1\" style=\"--n-tabs-title-order: 1;\" class=\"e-active elementor-element elementor-element-7c266261 e-con-full e-flex e-con e-child\" data-id=\"7c266261\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-2d15ec07 e-flex e-con-boxed e-con e-child\" data-id=\"2d15ec07\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5bcc8f37 elementor-widget elementor-widget-text-editor\" data-id=\"5bcc8f37\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">LDA (Latent Dirichlet Analysis)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Produc\u0103tor\/Autor: Blei\u00a0 et al., 2003; Li-Jia &amp; Fei-Fei, 2007;<\/p><p><em>Latent Dirichlet Analysis<\/em> este un model utilizat pentru clusterizarea unui corpus. Poate fi implementat un proces generativ de clusterizare nesupervizat\u0103 a fluxului de \u0219tiri pentru determinarea automat\u0103 a similarit\u0103\u021bilor detectabile \u00een corpus. Intui\u021bia principal\u0103 a acestei tehnici este c\u0103 putem asocia \u00een mod automat fiec\u0103rui cuv\u00e2nt o probabilitate de a semnala un tip de similaritate \u00eentre cuvinte manifestat\u0103 \u00een corpus. Ceea ce se ob\u021bine este un vector de probabilit\u0103\u021bi pentru fiecare cuv\u00e2nt, dimensiunea vectorului fiind determinat\u0103 de num\u0103rul de tipuri de similarit\u0103\u021bi considerate. Intrarea este constituit\u0103 de dou\u0103 variabile:\u00a0<\/p><ol><li><em>Num\u0103rul de tipuri de similarit\u0103\u021bi dorite<\/em>.\u00a0<\/li><li><em>Corpusul de analizat.\u00a0<\/em><\/li><\/ol><p>Men\u021bion\u0103m faptul c\u0103 pentru o predic\u021bie bun\u0103 de \u00eencadrare a \u0219tirilor, se poate alege un prag de p\u00e2n\u0103 la 50 topice iar pentru definirea similarit\u0103\u021bii la nivel de cuvinte &#8211; o re\u021bea neuronal\u0103 recurent\u0103. Aceast\u0103 clusterizare paralel\u0103 a cuvintelor, independent de topice, este cuplat\u0103 la intrarea LDA pentru clusterizarea corpusului \u00eentr-o manier\u0103 care reduce fenomenul <em>data sparseness<\/em>, una dintre problemele cheie \u00een construc\u021bia \u00eenv\u0103\u021b\u0103rii automate (ML) de tip n-gram, chiar \u0219i atunci c\u00e2nd dispunem de colec\u021bii mari de texte.<\/p><p><em>Indiferent c\u00e2t de mare este corpusul de antrenare, vor fi n-grame care nu vor ap\u0103rea \u00een el, \u00eens\u0103 care ar putea s\u0103 apar\u0103 \u00een corpusul de testare<\/em>.\u00a0<\/p><p>Un model de limb\u0103 de tip n-gram se construie\u0219te estim\u00e2nd probabilitatea secven\u021bei de cuvinte <em>W = w1, w2, \u2026, wn<\/em>\u00a0 pe baza unor corpusuri de text de mari dimensiuni.\u00a0<\/p><p>Ex: \u00een cazul unui ML bi-gram trebuie estimate probabilit\u0103\u021bile pentru fiecare pereche de cuvinte (<em>wi, wj<\/em>). Pentru a calcula aceste probabilit\u0103\u021bi se utilizeaz\u0103 principiul maximum <em>likelihood<\/em>. Cu alte cuvinte, se num\u0103r\u0103 de c\u00e2te ori cuv\u00e2ntul <em>wi<\/em> este urmat de cuv\u00e2ntul <em>wj<\/em>, comparativ cu alte cuvinte:\u00a0<\/p><p><img decoding=\"async\" src=\"https:\/\/lh6.googleusercontent.com\/9WRJW2V1oQ1M4QPpIvisHnSN2co2lxrT169qFJXZsw9YmForwgCfT7LrWSxrqBW5MdHgwvQmL9ziVFm6vlGz7OQA6R5NFgnFE3X2RIGKb7oQ3-BTZ3oJYb3l3HN-JCQg_KN8YukIXxm-lVTW\" \/><\/p><p>Practic, probabilitatea asignat\u0103 n-gramelor necunoscute este 0.\u00a0<\/p><p>\u00cen afar\u0103 de acest caz exist\u0103 alte n-grame care apar de foarte pu\u021bine ori (mai pu\u021bin de zece ori) \u00een corpusul de antrenare. Aceast\u0103 problem\u0103 devine mai important\u0103 \u00een cazul ML de tip n-gram de ordin mai mare. \u00cen acest caz, probabilit\u0103\u021bile care au fost estimate pe baza num\u0103rului de apari\u021bii ale n-gramelor \u00een corpusul de antrenare, trebuie ajustate. Cum? Prin <em>metode de netezire<\/em>.\u00a0<\/p><p>Metodele de netezire extrag o parte din probabilitatea alocat\u0103 pentru n-gramele \u00eent\u00e2lnite la antrenare \u0219i o redistribuie n-gramelor necunoscute.\u00a0<\/p><p>\u00cen literatur\u0103 \u00eent\u00e2lnim mai multe metode de netezire care particularizeaz\u0103 modul de redistribu\u021bie a probabilit\u0103\u021bii. Cea mai eficient\u0103 metod\u0103 este <strong><em>Good-Turing<\/em><\/strong>, cunoscut\u0103 drept <strong><em>netezirea Katz<\/em><\/strong>.\u00a0<\/p><p>Problema <em>data sparseness<\/em> este abordat\u0103 cu metode de <strong><em>back-off<\/em><\/strong>. Pentru a crea un model de limb\u0103 interpolat, metodele <strong><em>back-off<\/em><\/strong> utilizeaz\u0103 mai multe modele de limb\u0103 care au avantaje diferite \u0219i pot beneficia de toate p\u0103r\u021bile constituente. Cu alte cuvinte, pentru a determina probabilitatea unei n-grame care nu se reg\u0103se\u0219te \u00een corpusul de antrenare, se poate lua \u00een considerare \u0219i probabilitatea oferit\u0103 de modelele de limb\u0103 de ordin inferior. \u00cen acest caz, problema de optimizare este reprezentat\u0103 de alegerea echilibrului corect \u00eentre modelele de ordin superior \u0219i cele de ordin inferior, \u00een cazul \u00een care acestea vor fi folosite.\u00a0<\/p><p>Ex: Dac\u0103 modelele de tip n-gram de ordin mai mare ofer\u0103 un context de predictibilitate mai mare, modelele de ordin mai mic sunt mai robuste.\u00a0<\/p><p>O metod\u0103 de <strong><em>back-off<\/em><\/strong> eficient\u0103 \u00een estimarea probabilit\u0103\u021bilor n-gramelor necunoscutepe baza probabilit\u0103\u0163ilor asignate acestor n-grame de c\u0103tre modele de ordin mai mic este metoda modificat\u0103 de netezire <strong><em>Kneser-Ney<\/em><\/strong> (Chen &amp; Goodman, 1998). Aceasta folose\u0219te o metod\u0103 numit\u0103 reducere absolut\u0103 pentru a mic\u0219ora probabilitatea cumulat\u0103 a evenimentelor \u00eent\u00e2lnite.<\/p><p>\u00cen cazul unui corpus de \u0219tiri necesar pentru prezicerea interesului public pentru anumite topice, se maximizeaz\u0103 probabilitatea de ocuren\u021b\u0103 a unui cuv\u00e2nt \u021bin\u00e2nd cont de contextul imediat \u00eenconjur\u0103tor: outputul ar urma s\u0103 fie format dintr-un set de cuvinte cheie pentru fiecare topic.<\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Blei D.M., Ng A. Y., Jordan M. I. (2003), <em>Latent Dirichlet Allocation<\/em>, Journal of Machine Learning Research, 3: 993\u20131022.<\/li><li>Li-Jia L., Fei-Fei L. (2007), <em>What, where and who? classifying events by scene and object recognition<\/em>. In: Int. Conf. of Computer Vision: 221-228.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925062\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925062\" data-tab-index=\"2\" style=\"--n-tabs-title-order: 2;\" class=\" elementor-element elementor-element-451468e e-con-full e-flex e-con e-child\" data-id=\"451468e\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-847a31c e-flex e-con-boxed e-con e-child\" data-id=\"847a31c\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-16b270a elementor-widget elementor-widget-text-editor\" data-id=\"16b270a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Support Vector Machine (SVM)<\/h3><div class=\"row\"><div class=\"row\"><p>\u00a0<\/p><\/div><\/div><div class=\"row\"><div class=\"row item\"><div class=\"col-2\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Produc\u0103tor\/Autor: Nello &amp; Shawe-Taylor, 2000; Tong &amp; Chang, 2001;<\/p><p>SVM este un model \u0219i o tehnic\u0103 de clasificare a datelor, care presupune existen\u021ba unui set de date pentru antrenare \u0219i un set de date de testare. Fiecare instan\u021b\u0103 din setul de antrenare este deja clasificat\u0103 ca apar\u021bin\u00e2nd unei anumite clase, iar acest set de date este folosit pentru a crea un model care este capabil s\u0103 eticheteze instan\u021bele din setul de testare ca apar\u021bin\u00e2nd unei anumite clase. SVM caut\u0103 solu\u021bia ca \u00eentr-o problem\u0103 de optimizare.<\/p><p>Exemplu de aplica\u021bie: clasificarea semnalelor (slabe vs. non-slabe)<\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Tong S., Chang E. (2001), <em>Support vector machine active learning for image retrieval<\/em>.<\/li><li>Nello C., Shawe-Taylor J., (2000), <em>An Introduction to Support Vector Machines and other kernel based learning methods<\/em>.<\/li><\/ul><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925063\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925063\" data-tab-index=\"3\" style=\"--n-tabs-title-order: 3;\" class=\" elementor-element elementor-element-27bdfcf e-con-full e-flex e-con e-child\" data-id=\"27bdfcf\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-2019262 e-flex e-con-boxed e-con e-child\" data-id=\"2019262\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f41e416 elementor-widget elementor-widget-text-editor\" data-id=\"f41e416\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Natural Language Toolkit (NLTK)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Autori: Steven Bird (Australia), Edward Loper (USA), Ewan Klein (USA), etc.;\u00a0<\/p><p>A fost dezvoltat la Universitatea din Pennsylvania<\/p><p>NLTK reprezint\u0103 o multitudine de module program <em>open source<\/em>, tutoriale \u0219i probleme, oferind cursuri de lingvistic\u0103 computa\u021bional\u0103. NLTK acoper\u0103 procesarea limbajului natural (simbolic\u0103 \u0219i statistic\u0103), fiind \u0219i o interfa\u021b\u0103 la corpusuri adnotate. NLTK ruleaz\u0103 pe toate platformele suportate de Python, inclusiv Windows, OS X, Linux \u0219i Unix. Tipul resursei: platform\u0103; Scop: construirea de aplica\u021bii de prelucrare a limbajului uman \u00een limbajul <strong>Python<\/strong> (<em>text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning<\/em>)<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Loper Edward, Bird Steven, (2004), <em>NLTK: The Natural Language Toolkit<\/em>. ACL (<a href=\"https:\/\/www.aclweb.org\/anthology\/W02-0109\"><u>https:\/\/www.aclweb.org\/anthology\/W02-0109<\/u><\/a>)<\/li><li>Bird Steven, Loper Edward and Klein Ewan (2009), <em>Natural Language Processing with Python<\/em>. O\u2019Reilly Media Inc. (<a href=\"http:\/\/nltk.org\/book\"><u>http:\/\/nltk.org\/book<\/u><\/a>)<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925064\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925064\" data-tab-index=\"4\" style=\"--n-tabs-title-order: 4;\" class=\" elementor-element elementor-element-71c079b e-con-full e-flex e-con e-child\" data-id=\"71c079b\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-d870cd8 e-flex e-con-boxed e-con e-child\" data-id=\"d870cd8\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c468dbf elementor-widget elementor-widget-text-editor\" data-id=\"c468dbf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Graph networks (Relational Neural Networks)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>The method \u201cgeneralizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors\u201d. Tipul resursei: model\/metod\u0103; Scop: ra\u021bionament neural care lucreaz\u0103 cu rela\u021bii (\u00een grafuri) \u00een loc de caracteristici.<\/p><p>Produc\u0103tor: DeepMind<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Battaglia PeterW., Pascanu Razvan , Lai Matthew, Rezende Danilo, Kavukcuoglu Koray(2016):<a href=\"https:\/\/arxiv.org\/pdf\/1612.00222v1.pdf\"> <em><u>Interaction Networks for Learning about Objects, Relations and Physics<\/u><\/em><\/a>.<\/li><li>Battaglia <em>et al<\/em>. (2018), <em>Relational inductive biases, deep learning, and graph network<\/em>s =&gt;<a href=\"https:\/\/arxiv.org\/pdf\/1806.01261.pdf\"> <u>https:\/\/arxiv.org\/pdf\/1806.01261.pdf<\/u><\/a><\/li><li>Kipf Thomas (2016), <em>Graph convolutional networks<\/em>:<a href=\"http:\/\/tkipf.github.io\/graph-convolutional-networks\/\"> <u>http:\/\/tkipf.github.io\/graph-convolutional-networks\/<\/u><\/a><\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925065\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925065\" data-tab-index=\"5\" style=\"--n-tabs-title-order: 5;\" class=\" elementor-element elementor-element-4af84b1 e-con-full e-flex e-con e-child\" data-id=\"4af84b1\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-ef90191 e-flex e-con-boxed e-con e-child\" data-id=\"ef90191\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-948272b elementor-widget elementor-widget-text-editor\" data-id=\"948272b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">NLP-Cube<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Bibliotec\u0103 open-source de prelucr\u0103ri a limbajului natural (<em>Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging, Dependency Parsing and Named Entity Recognition<\/em>) bazat\u0103 pe modele Deep Learning. Tipul resursei: platform\u0103.<\/p><p>Produc\u0103tor: Adobe;<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Boro\u0219, Tiberiu and \u0218tefan Daniel Dumitrescu and Ruxandra Burtic\u0103 (2018),<a href=\"http:\/\/www.aclweb.org\/anthology\/K18-2017\"> <em><u>NLP-Cube: End-to-End Raw Text Processing With Neural Networ<\/u><\/em><\/a><em><u>ks<\/u><\/em>, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Association for Computational Linguistics. p. 171&#8211;179.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925066\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925066\" data-tab-index=\"6\" style=\"--n-tabs-title-order: 6;\" class=\" elementor-element elementor-element-cb2a348 e-con-full e-flex e-con e-child\" data-id=\"cb2a348\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-da496d2 e-flex e-con-boxed e-con e-child\" data-id=\"da496d2\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d550198 elementor-widget elementor-widget-text-editor\" data-id=\"d550198\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">GloVe embeddings<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>GloVe este o metoda nesupervizat\u0103 de ob\u021binere de reprezent\u0103ri vectoriale pentru cuvinte. Antrenarea se realizeaz\u0103 pe o matrice de co-ocuren\u021be \u00eentre cuvinte, extras\u0103 dintr-un corpus. Reprezentarea rezultat\u0103 are rolul de a surprinde rela\u021bii \u00eentre cuvinte cu sensuri asem\u0103n\u0103toare sau care se reg\u0103sesc \u00een contexte similare. La adresa indicat\u0103 se pot desc\u0103rca modele pentru diverse limbi. Tipul resursei: model; Scop: ob\u021binerea reprezent\u0103rilor vectoriale pentru cuvintele unui corpus.\u00a0<\/p><p>Produc\u0103tor: Stanford NLP<\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Pennington Jeffrey, Richard Socher, Christopher D. Manning (2014), <em>GloVe: Global Vectors for Word Representation<\/em>, in EMNLP, <a href=\"https:\/\/nlp.stanford.edu\/pubs\/glove.pdf\">\u00a0<u>https:\/\/nlp.stanford.edu\/pubs\/glove.pdf<\/u><\/a>.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925067\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925067\" data-tab-index=\"7\" style=\"--n-tabs-title-order: 7;\" class=\" elementor-element elementor-element-61d2dfe e-con-full e-flex e-con e-child\" data-id=\"61d2dfe\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-b473380 e-flex e-con-boxed e-con e-child\" data-id=\"b473380\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9de7ac6 elementor-widget elementor-widget-text-editor\" data-id=\"9de7ac6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">ELMo embeddings<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>ELMo este o metod\u0103 de reprezentare vectorial\u0103 a cuvintelor care modeleaz\u0103 caracteristici complexe ale cuvintelor (ex. sintaxa sau semantic\u0103) \u0219i modul \u00een care acestea variaz\u0103 \u00een diverse contexte lingvistice. Astfel, se propune o solu\u021bie pentru problema polisemiei cuvintelor. Modelele pre-antrenate pot fi ad\u0103ugate cu u\u0219urin\u021b\u0103 peste reprezent\u0103ri deja existente. S-a demonstrat experimental c\u0103 ELMo ajut\u0103 la \u00eembun\u0103t\u0103\u021birea multor rezultate state-of-the-art pentru mai multe probleme de procesare textual\u0103. Tipul resursei: model; Scop: modelarea caracteristicilor sintactice \u0219i\/sau semantice ale cuvintelor dintr-un corpus.<\/p><p>Produc\u0103tor: Allen NLP<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Peters Matthew E., Mark Neumann, Luke Zettlemoyer, Wen-tau Yih (2018), <em>Dissecting Contextual Word Embeddings: Architecture and Representation<\/em>, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, p. 1499\u20131509, Association for Computational Linguistics.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925068\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925068\" data-tab-index=\"8\" style=\"--n-tabs-title-order: 8;\" class=\" elementor-element elementor-element-d0ca672 e-con-full e-flex e-con e-child\" data-id=\"d0ca672\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-0f51741 e-flex e-con-boxed e-con e-child\" data-id=\"0f51741\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a7a0433 elementor-widget elementor-widget-text-editor\" data-id=\"a7a0433\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Character-level embeddings<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Fiecare simbol din text este reprezentat sub forma unui vector al c\u0103rui num\u0103r de elemente este egal cu num\u0103rul de simboluri distincte din text, transform\u00e2nd astfel textul dintr-o secven\u021b\u0103 de simboluri (litere \u0219i caractere speciale) \u00eentr-o secven\u021b\u0103 de vectori.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Zhang Xiang , Yann LeCun (2016), <em>Text Understanding from Scratch<\/em>. Online:<a href=\"https:\/\/arxiv.org\/pdf\/1502.01710.pdf\"> <u>https:\/\/arxiv.org\/pdf\/1502.01710.pdf<\/u><\/a><\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-5945925069\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-5945925069\" data-tab-index=\"9\" style=\"--n-tabs-title-order: 9;\" class=\" elementor-element elementor-element-60d3add e-con-full e-flex e-con e-child\" data-id=\"60d3add\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-7d670e4 e-flex e-con-boxed e-con e-child\" data-id=\"7d670e4\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2e173c3 elementor-widget elementor-widget-text-editor\" data-id=\"2e173c3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">TensorFlow<\/h3><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Platform\u0103 <em>open-source<\/em> pentru \u00eenv\u0103\u021bare automat\u0103, care pune la dispozi\u021bia utilizatorului un set complet de componente necesare pentru construirea modelelor bazate pe re\u021bele neuronale. Tipul resursei: platform\u0103; Exemplu de utilizare: antrenarea unui model neuronal pentru segmentarea clauzelor din text.<\/p><p>Produc\u0103tor: ini\u021bial Google; \u00een prezent proiectul este open-source<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Abadi M., Agarwal, A., Barham, P., Brevdo, E., Chen,Ashish Agarwal et al.\u00a0 (2015), <em>TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems<\/em>,<a href=\"http:\/\/download.tensorflow.org\/paper\/whitepaper2015.pdf\"><u>http:\/\/download.tensorflow.org\/paper\/whitepaper2015.pdf<\/u><\/a><\/li><\/ul><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250610\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250610\" data-tab-index=\"10\" style=\"--n-tabs-title-order: 10;\" class=\" elementor-element elementor-element-2b94589 e-con-full e-flex e-con e-child\" data-id=\"2b94589\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-8052b69 e-flex e-con-boxed e-con e-child\" data-id=\"8052b69\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a35bec8 elementor-widget elementor-widget-text-editor\" data-id=\"a35bec8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Seq2seq (Sequence to sequence learning)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Modelul este compus din dou\u0103 p\u0103r\u021bi:<\/p><p>\u2013 Codificatorul\/codorul care prime\u0219te la intrare o propozi\u021bie \u00een limba englez\u0103 \u0219i o transform\u0103 \u00eentr-o reprezentare vectorial\u0103.<\/p><p>\u2013 Decodorul care prime\u0219te reprezentarea vectorial\u0103 a propozi\u021biei \u00een limba surs\u0103 \u0219i o transform\u0103 \u00een propozi\u021bia corespunz\u0103toare din limba \u021bint\u0103.<\/p><p>At\u00e2t codificatorul c\u00e2t \u0219i decodorul au la baza celule <em>Long Short-Term Memory<\/em> (v. fi\u0219a <strong>Long Short-Term Memory<\/strong>).<\/p><p>Autori: Ilya Sutskever, Oriol Vinyals, and Quoc V. Le<\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Sutskever, Ilya and Vinyals Oriol and V. Le Quoc (2014), <em>Sequence to sequence learning with neural networks<\/em>, In Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13, Montreal, Quebec, Canada, pages 3104\u20133112.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250611\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250611\" data-tab-index=\"11\" style=\"--n-tabs-title-order: 11;\" class=\" elementor-element elementor-element-18bc764 e-con-full e-flex e-con e-child\" data-id=\"18bc764\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-44740a6 e-flex e-con-boxed e-con e-child\" data-id=\"44740a6\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-929e979 elementor-widget elementor-widget-text-editor\" data-id=\"929e979\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Long Short-Term Memory<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Re\u021bea neuronal\u0103 recurent\u0103, specializat\u0103 pentru procesarea de secven\u021be, dotat\u0103 cu memorie intern\u0103 \u0219i mecanisme de acces la memorie numite ecluze\/por\u021bi (<em>gates<\/em> \u00een englez\u0103). Ecluzele reglementeaz\u0103 cantitatea de informa\u021bie care este stocat\u0103 \u00een memoria re\u021belei, cantitatea de informa\u021bie care este \u0219tears\u0103 din memorie \u0219i &#8211; deoarece re\u021beaua se ocup\u0103 de procesarea de secven\u021be &#8211; cantitatea de informa\u021bie util\u0103 pentru procesarea urm\u0103torului element din secven\u021b\u0103. \u00cen cazul experimentului de segmentare \u00een clauze (a se vedea exemplul din sec\u021biunea <em>Alte informa\u021bii utile<\/em>), codificatorul re\u021bine, pentru fiecare cuv\u00e2nt din fraz\u0103, informa\u021biile necesare pentru a crea o reprezentare vectorial\u0103, iar \u00een cazul decodorului &#8211; informa\u021bia necesar\u0103 pentru a genera urm\u0103torul element din fraza care include \u0219i marcajele de sf\u00e2r\u0219it de segment.\u00a0<\/p><p>Tipul resursei: model. Scop: codificarea textului \u00een puncte ale unui spa\u021biu vectorial \u0219i decodificarea reprezent\u0103rii vectoriale \u00een text.<\/p><p>Autori: Sepp Hochreiter, J\u00fcrgen Schmidhuber.<\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Hochreiter Sepp, Ju\u0308rgen Schmidhuber (1997), <em>Long short-term memory. Neural Computation<\/em>, 9(8):1735\u20131780.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250612\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250612\" data-tab-index=\"12\" style=\"--n-tabs-title-order: 12;\" class=\" elementor-element elementor-element-a4fc7b2 e-con-full e-flex e-con e-child\" data-id=\"a4fc7b2\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-1957330 e-flex e-con-boxed e-con e-child\" data-id=\"1957330\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1b354f9 elementor-widget elementor-widget-text-editor\" data-id=\"1b354f9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">TensorBoard<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>TensorBoard ofer\u0103 posibilitatea de a urm\u0103ri evolu\u021bia modelului antrenat prin crearea de grafice care afi\u0219eaz\u0103 diverse metrici selectate de utilizator la dezvoltarea modelului. \u00cen plus, TensorBoard ofer\u0103 posibilitatea de a vizualiza \u00eentreg graful computa\u021bional care st\u0103 la baza modelului antrenat. Tipul resursei: component\u0103 TensorFlow; Scop: vizualizarea evolu\u021biei modelului antrenat.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>https:\/\/www.tensorflow.org\/tensorboard\/r1\/overview.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250613\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250613\" data-tab-index=\"13\" style=\"--n-tabs-title-order: 13;\" class=\" elementor-element elementor-element-949163d e-con-full e-flex e-con e-child\" data-id=\"949163d\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-450b7f6 e-flex e-con-boxed e-con e-child\" data-id=\"450b7f6\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-023c805 elementor-widget elementor-widget-text-editor\" data-id=\"023c805\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Word2Vec<\/h3><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Exist\u0103 dou\u0103 modele Word2Vec (ambele implic\u00e2nd re\u021bele neuronale): <em>Skip Gram<\/em> \u0219i <em>Common Bag of Words<\/em> (CBOW).<\/p><p>Modelul <strong>CBOW<\/strong>: contextul fiec\u0103rui cuv\u00e2nt este considerat intrare, ie\u0219irea \u00eencerc\u00e2nd s\u0103 prezic\u0103 cuv\u00e2ntul corespunz\u0103tor contextului. S\u0103 lu\u0103m \u00een considerare exemplul nostru: \u201eAi o zi minunat\u0103\u201d.<\/p><p>L\u0103s\u00e2nd intrarea \u00een re\u021beaua neural\u0103 s\u0103 fie cuv\u00e2ntul \u201eminunat\u201d, s\u0103 observ\u0103m c\u0103 aici \u00eencerc\u0103m s\u0103 prezicem un cuv\u00e2nt \u021bint\u0103 (\u201czi\u201d) folosind un singur cuv\u00e2nt de intrare \u00een context. Mai precis, utiliz\u0103m codificarea cuv\u00e2ntului de intrare \u0219i m\u0103sur\u0103m eroarea de ie\u0219ire \u00een compara\u021bie cu o singur\u0103 codificare a cuv\u00e2ntului \u021bint\u0103 (\u201czi\u201d). \u00cen procesul de predic\u021bie a cuv\u00e2ntului \u021bint\u0103, \u00eenv\u0103\u021b\u0103m reprezentarea vectorului cuv\u00e2ntului \u021bint\u0103.<\/p><p><img decoding=\"async\" src=\"https:\/\/lh5.googleusercontent.com\/FHsVIcVqqztwzmS058RLY0NHDkxBrokBrVna7YUHWzc-a4Cjf6wTXNLUPnswHCCaW4pJYE66nTz4P8icdLkr3BMx0B2fVKb5T5wqj4JUvhkDtBctdsZL1DBXNB4jrcFTYy9UATBJ3FOppJ-T\" \/><\/p><p>Fig. 1. Arhitectura unui model CBOW simplu, cu un singur cuv\u00e2nt \u00een context\u00a0<\/p><p>(Rong, 2014)<\/p><p>\u00a0<\/p><p>Cuv\u00e2ntul de intrare sau context este un vector codat cu dimensiunea <em>V<\/em>, stratul ascuns con\u021bine <em>N<\/em> neuroni \u0219i ie\u0219irea este, din nou, un vector de lungime <em>V.<\/em><\/p><p>A\u0219adar, se poate observa maniera \u00een care se genereaz\u0103 reprezent\u0103ri de cuvinte utiliz\u00e2nd cuvintele de context. Dar exist\u0103 \u00eenc\u0103 o cale: putem folosi cuv\u00e2ntul \u021bint\u0103 (a c\u0103rui reprezentare vrem s\u0103 gener\u0103m) s\u0103 prezicem contextul, iar \u00een acest proces, producem reprezent\u0103rile. O alt\u0103 variant\u0103, numit\u0103 modelul Skip Gram, face acest lucru.<\/p><p>Tipul resursei: model; Scop: construirea de <em>word embeddings.<\/em><\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Mikolov Tomas, Sutskever Ilya, Chen Kai, Corrado Greg, Dean, Jeffrey (2013), <em>Distributed Representations of Words and Phrases and their Compositionality<\/em>, <a href=\"https:\/\/arxiv.org\/abs\/1310.4546\">\u00a0<u>https:\/\/arxiv.org\/abs\/1310.4546<\/u><\/a><\/li><li>Levy Omer, Goldberg Yoav (2014),<a href=\"https:\/\/levyomer.files.wordpress.com\/2014\/04\/linguistic-regularities-in-sparse-and-explicit-word-representations-conll-2014.pdf\"> <em>Linguistic Regularities in Sparse and Explicit Word\u00a0 Representations<\/em><\/a>.<\/li><\/ul><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250614\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250614\" data-tab-index=\"14\" style=\"--n-tabs-title-order: 14;\" class=\" elementor-element elementor-element-c74f4c5 e-con-full e-flex e-con e-child\" data-id=\"c74f4c5\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-18aea40 e-flex e-con-boxed e-con e-child\" data-id=\"18aea40\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-349fef7 elementor-widget elementor-widget-text-editor\" data-id=\"349fef7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">PyMagnitude<\/h3><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Este un pachet dezvoltat cu inten\u021bia de a folosi vector embeddings \u00een machine learning \u0219i de a oferi o alternativ\u0103 mai simpl\u0103 \u0219i mai rapid\u0103 pentru <strong><em>Gensim<\/em><\/strong>. Principala caracteristic\u0103 este utilizarea unui format unic de documente (<em>.magnitude<\/em>) care ajut\u0103 foarte mult la \u00eembun\u0103t\u0103\u021birea timpului de c\u0103utare \u0219i de \u00eenc\u0103rcare a vectorilor, \u00een func\u021bie de chei. Este folosit SQLite pentru a stoca \u0219i indexa datele, astfel timpul de c\u0103utare se \u00eembun\u0103t\u0103\u021be\u0219te de la rulare la rulare.\u00a0<\/p><p>Pachetul permite concatenarea mai multor modele (tip <em>.magnitude<\/em>) \u0219i ofer\u0103 func\u021bionalit\u0103\u021bi, precum:\u00a0<\/p><ul><li>\u00a0<em>Query<\/em> \u2013 pentru un cuv\u00e2nt\/ mai multe cuvinte;<\/li><li><em>Similarity<\/em> \u2013 pentru indicele de similaritate \u00eentre dou\u0103 cuvinte;<\/li><li><em>Most_Similar<\/em> \u2013 pentru a ob\u021bine cuv\u00e2ntul cel mai similar cu cel dat ca input;<\/li><li><em>POS tags<\/em> \u0219i <em>Syntax Dependencies<\/em> \u2013 returneaz\u0103 vectori \u00een func\u021bie de argumentele de POS \u0219i rela\u021biile de dependen\u021b\u0103 furnizate;<\/li><li><em>(.bin .txt .vec .hdf5) to (.magnitude) converter<\/em> \u2013 transform\u0103 fi\u0219ierele \u00een fi\u0219iere .magnitude<\/li><\/ul><p>Formatul unui astfel de fi\u0219ier este similar cu cel al unui dic\u021bionar: exist\u0103 o list\u0103 de <em>keys<\/em> (cuvintele) \u0219i pentru fiecare cuv\u00e2nt reprezentarea <em>word2vec<\/em> pe <em>x<\/em> coloane (50, 100, 200, 300 etc.).<\/p><p>Pachetul este dezvoltat pentru Python \u0219i folose\u0219te biblioteca <em>numPy<\/em> pentru vectori. Este folosit, de obicei, pentru a crea modele care folosesc reprezent\u0103ri vectoriale. Mai poate fi folosit pentru a corecta gre\u0219elile de scriere \u0219i cuvintele din afara vocabularului (folosind func\u021biile <em>most_similar <\/em>\u0219i <em>similarity<\/em>). Tipul resursei: <em>Libr\u0103rie<\/em>; Scopul utiliz\u0103rii: <em>Word Embeddings.<\/em><\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><p>&#8211;<\/p><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250615\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250615\" data-tab-index=\"15\" style=\"--n-tabs-title-order: 15;\" class=\" elementor-element elementor-element-7a51881 e-con-full e-flex e-con e-child\" data-id=\"7a51881\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-326f50a e-flex e-con-boxed e-con e-child\" data-id=\"326f50a\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8a7bbb7 elementor-widget elementor-widget-text-editor\" data-id=\"8a7bbb7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">CTC: Connectionist Temporal Classification<\/h3>\n<div class=\"row item\">\n<p class=\"col-2\">Descriere<\/p>\n\nMulte sarcini de \u00eenv\u0103\u021bare a secven\u021belor din lumea real\u0103 necesit\u0103 predic\u021bia secven\u021belor de etichete din date de intrare zgomotoase, nesegmentate. \u00cen recunoa\u0219terea vorbirii, de exemplu, semnalul acustic este transcris \u00een cuvinte sau subcuvinte (unit\u0103\u021bi). Re\u021belele neuronale recurente (RNN) reu\u0219esc s\u0103 \u00eenve\u021be cu succes secven\u021be, deci ar p\u0103rea potrivite pentru astfel de sarcini. Cu toate acestea, pentru c\u0103 au nevoie de date de antrenament pre-segmentate \u0219i post-procesate pentru a transforma rezultatele lor \u00een secven\u021be de etichete, aplicabilitatea lor a fost limitat\u0103. Aceast\u0103 metod\u0103 propune o nou\u0103 metod\u0103 de antrenare a RNN-urilor pentru a eticheta direct secven\u021be nesegmentate, rezolv\u00e2nd astfel ambele probleme.<\/h1>\nExemple de utilizare sunt \u00een sisteme S2T, la decodificarea imaginilor care con\u021bin scris de tipar sau de m\u00e2n\u0103 etc.<\/h1>\nautor: (Alex Graves et al, 2006)\n\n<\/div>\n<div class=\"col-2\">Referin\u021be<\/div>\n<div class=\"col-7\">\n<ul>\n \t<li><a href=\"https:\/\/scholar.google.ro\/citations?user=DaFHynwAAAAJ&amp;hl=en&amp;oi=sra\">A. Graves<\/a>, <a href=\"https:\/\/scholar.google.ro\/citations?user=1ctMdh8AAAAJ&amp;hl=en&amp;oi=sra\">S. Fern\u00e1ndez<\/a>, <a href=\"https:\/\/scholar.google.ro\/citations?user=RsH53ecAAAAJ&amp;hl=en&amp;oi=sra\">F. Gomez<\/a> (2006). <a href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/1143844.1143891\">Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks<\/a>, Proceedings of the 23rd international conference on Machine learning, link: https:\/\/mediatum.ub.tum.de\/doc\/1292048\/file.pdf<\/li>\n<\/ul>\n<\/div>\n<div class=\"row\">\n<p class=\"display-4\">CTC: Connectionist Temporal Classification<\/p>\n\n<div class=\"row\">\n\n&nbsp;\n\n<\/div>\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250616\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250616\" data-tab-index=\"16\" style=\"--n-tabs-title-order: 16;\" class=\" elementor-element elementor-element-290a33c e-con-full e-flex e-con e-child\" data-id=\"290a33c\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-f6dc286 e-flex e-con-boxed e-con e-child\" data-id=\"f6dc286\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-edb337d elementor-widget elementor-widget-text-editor\" data-id=\"edb337d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">RoBERT \u2013 A Romanian BERT Mode<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Se introduce un model BERT preinstruit doar \u00een limba rom\u00e2n\u0103 \u2013 ROBERT \u2013 care este comparat cu diferite modele multilingve pe \u0219apte sarcini PLN specifice limbii rom\u00e2ne, grupate \u00een trei categorii \u0219i anume: analiza sentimentelor, identificarea dialectelor \u0219i a subiectelor \u00eencruci\u0219ate \u0219i refacerea diacriticelor. \u00cen vederea preinstruirii modelului RoBERT, a fost construit un corpus rom\u00e2nesc, extras din mai multe surse, variind de la text aleatoriu, accesat cu crawlere de pe Internet, la surse mai formale (ex: Wikipedia, c\u0103r\u021bi sau ziare). Corpusul a fost alc\u0103tuit din trei surse principale: <strong>Romanian Wikipedia dump<\/strong>, un corpus rom\u00e2nesc creat de Oscar (Javier Ortiz Suarez et al., 2019), \u00eempreun\u0103 cu colec\u021bia <strong>RoTex<\/strong> (https:\/\/github.com\/aleris\/ReadME-RoTex-Corpus-Builder from which the following sources were considered: \u201dbiblior\u201d, \u201dbiblioteca-digitala-ase\u201d, \u201dbestseller-md\u201d, \u201dlitera-net\u201d, \u201dbzi\u201d, \u201ddcep\u201d, \u201ddezbateri-parlamentare\u201d, \u201ddgt-aquis\u201d, \u201dpaul-goma\u201d, \u201drudolf-steiner\u201d and \u201dziarul-lumina\u201d). Modelul dep\u0103\u0219e\u0219te modelele multilingve, precum \u0219i o alt\u0103 implementare monolingv\u0103 a BERT<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li><a href=\"https:\/\/dblp.org\/pid\/206\/1244.html\">Mihai Masala<\/a>, <a href=\"https:\/\/dblp.org\/pid\/118\/3624.html\">Stefan Ruseti<\/a>, Mihai Dascalu (2020). RoBERT &#8211; A Romanian BERT Model. <a href=\"https:\/\/dblp.org\/db\/conf\/coling\/coling2020.html#MasalaRD20\">COLING 2020<\/a>: 6626-6637, link: <a href=\"https:\/\/aclanthology.org\/2020.coling-main.581.pdf\"><u>https:\/\/aclanthology.org\/2020.coling-main.581.pdf<\/u><\/a>\u00a0<\/li><li>Pedro Javier Ortiz Suarez et al. 2019. Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures. In the 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7).<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250617\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250617\" data-tab-index=\"17\" style=\"--n-tabs-title-order: 17;\" class=\" elementor-element elementor-element-b365397 e-con-full e-flex e-con e-child\" data-id=\"b365397\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-7f077cd e-flex e-con-boxed e-con e-child\" data-id=\"7f077cd\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-87edd77 elementor-widget elementor-widget-text-editor\" data-id=\"87edd77\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Re\u021bele neuronale recurente de tip codor-decodor (RNN encoder-decoders)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>\u00cen paradigma traducerii automate (TA), o re\u021bea neural\u0103 codoare cite\u0219te \u0219i codific\u0103 o fraz\u0103 din intrare dat\u0103 \u00een limba surs\u0103 \u00eentr-un vector de lungime fix\u0103, \u00een timp ce decodorul produce \u00een ie\u0219ire o traducere \u00een limba \u021bint\u0103 din vectorul codat. Perechea codor-decodor sunt antrenate pentru a produce traduceri corecte pentru perechea de limbi surs\u0103-\u021bint\u0103.<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Practic, intrarea de lungime variabil\u0103 este codat\u0103 mai \u00eent\u00e2i \u00eentr-un vector de lungime fix\u0103, acesta fiind apoi decodat \u00eentr-o fraz\u0103, de asemenea de lungime variabil\u0103.<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00cen abord\u0103rile lui [Cho et al., 2014] \u0219i [Sutskever et al., 2014], codorul cite\u0219te fraza de intrare, o secven\u021b\u0103 de vectori <em>x<\/em> = (<em>x<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>x<\/em><em><sub>Tx<\/sub><\/em>), \u00eentr-un vector <em>c<\/em>. Astfel, abordarea cea mai cunoscut\u0103 a re\u021belelor neuronale recurente este aceea \u00een care o stare ascuns\u0103 la momentul <em>t<\/em> este <em>h<\/em><em><sub>t<\/sub><\/em> \u2208 R<em><sup>n<\/sup><\/em> de forma:<\/p><p><em>h<\/em><em><sub>t<\/sub><\/em> = <em>f<\/em> (<em>x<\/em><em><sub>t<\/sub><\/em>, <em>h<\/em><em><sub>t<\/sub><\/em><sub>\u22121<\/sub>) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (1)<\/p><p>iar<\/p><p><em>c<\/em> = <em>q<\/em> ({<em>h<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>h<\/em><em><sub>Tx<\/sub><\/em> })\u00a0 \u00a0 \u00a0 \u00a0 (2)<\/p><p>este ie\u0219irea, de forma unui vector generat din secven\u021ba de st\u0103ri ascunse, cu <em>f<\/em> \u0219i <em>q<\/em> func\u021bii nelineare.<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00cen TA, decoderul este antrenat s\u0103 prezic\u0103 cuv\u00e2ntul urm\u0103tor <em>y<\/em><em><sub>t\u2019<\/sub><\/em> plec\u00e2nd de la vectorul context <em>c<\/em> \u0219i toate cuvintele prezise anterior {<em>y<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>y<\/em><em><sub>t\u2019<\/sub><\/em><sub>\u22121<\/sub>}. Cu alte cuvinte, decodorul define\u0219te o probabilitate peste traducerea <em>y<\/em> prin descompunerea probabilit\u0103\u021bii comune \u00een probabilit\u0103\u021bi condi\u021bionate ordonate:<\/p><p><em>p<\/em>(<em>y<\/em>) = PRODUS, cu <em>t<\/em> de la 1 la <em>T<\/em>, din <em>p<\/em>(<em>y<\/em><em><sub>t<\/sub><\/em> | {<em>y<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>y<\/em><em><sub>t<\/sub><\/em><sub>\u22121<\/sub>} , <em>c<\/em>), \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (3)<\/p><p>unde <em>y<\/em> = (<em>y<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>y<\/em><em><sub>Ty<\/sub><\/em>). Cu un RNN, fiecare probabilitate condi\u021bionat\u0103 este modelat\u0103 ca:<\/p><p><em>p<\/em>(<em>y<\/em><em><sub>t<\/sub><\/em> | {<em>y<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>y<\/em><em><sub>t<\/sub><\/em><sub>\u22121<\/sub>} , <em>c<\/em>) = <em>g<\/em>(<em>y<\/em><em><sub>t<\/sub><\/em><sub>\u22121<\/sub>, <em>s<\/em><em><sub>t<\/sub><\/em>, <em>c<\/em>), \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (4)<\/p><p>unde <em>g<\/em> este o func\u021bie neliniar\u0103, poten\u021bial multistratificat\u0103, care emite probabilitatea <em>y<\/em><em><sub>t<\/sub><\/em>, iar <em>s<\/em><em><sub>t<\/sub><\/em> este starea ascuns\u0103 a RNN.<\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Sutskever, I., Vinyals, O., and Le, Q. (2014). Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems (NIPS 2014).<\/li><li>Cho, K., van Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y. (2014a). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP 2014).<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250618\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250618\" data-tab-index=\"18\" style=\"--n-tabs-title-order: 18;\" class=\" elementor-element elementor-element-363bdf8 e-con-full e-flex e-con e-child\" data-id=\"363bdf8\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-3d66bd6 e-flex e-con-boxed e-con e-child\" data-id=\"3d66bd6\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5f7bc6a elementor-widget elementor-widget-text-editor\" data-id=\"5f7bc6a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Aten\u021bia (attention-based approaches)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>[Bahdanau et al., 2015] propun modelul de re\u021bea care prezice cuv\u00e2ntul \u021bint\u0103, \u021bin\u00e2nd cont cu predilec\u021bie de anumite cuvinte din fraza de intrare. Acest mecanism reflect\u0103 aten\u021bia focalizat\u0103, pe care oamenii o aplic\u0103 instinctiv \u00een enorm de multe activit\u0103\u021bi mentale, atunci c\u00e2nd o decizie se bazeaz\u0103 doar pe unele dintre semnele pe care le avem la dispozi\u021bie, anumite p\u0103r\u021bi ale tabloului pe care \u00eel decodific\u0103m fiind mai importante dec\u00e2t altele. De exemplu, \u00een paradigma traducerii automate, fraza din intrare poate fi mai lung\u0103 dec\u00e2t cele pe care a fost antrenat sistemul, ceea ce va produce erori \u00een traducere, pentru c\u0103 deteriorarea ie\u0219irii este accelerat\u0103 de lungimea intr\u0103rii [Cho et al., 2014a]. De accea, limitarea contextului, dublat\u0103 de focalizare, poate da rezultate mult mai bune.<\/p><p>Modelul propus de [Bahdanau et al., 2015] reprezint\u0103 o extensie a modelului codor-decodor, care aliniaz\u0103 \u0219i traduce simultan. De fiecare dat\u0103 c\u00e2nd un cuv\u00e2nt este generat \u00een traducere, se caut\u0103 un set de pozi\u021bii din propozi\u021bia surs\u0103 unde sunt concentrate cele mai relevante informa\u021bii. Cuv\u00e2ntul \u021bint\u0103 este astfel prezis pe baza vectorilor de context asocia\u021bi acestor pozi\u021bii surs\u0103 precum \u0219i a tuturor cuvintelor \u021bint\u0103 generate anterior.<\/p><p>\u00cen [<a href=\"https:\/\/arxiv.org\/search\/cs?searchtype=author&amp;query=Bahdanau%2C+D\">Bahdanau<\/a> et al., 2015] mai \u00eent\u00e2i se define\u0219te:<\/p><p>\u00a0<\/p><p>DECODORUL<\/p><p>Probabilit\u0103\u021bile condi\u021bionate din (3), detaliate \u00een (4), devin acum:<\/p><p><em>p<\/em>(<em>y<\/em><em><sub>i<\/sub><\/em> | <em>y<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>y<\/em><em><sub>i<\/sub><\/em><sub>\u22121<\/sub>, x) = <em>g<\/em>(<em>y<\/em><em><sub>i<\/sub><\/em><sub>\u22121<\/sub>, <em>s<\/em><em><sub>i<\/sub><\/em>, <em>c<\/em><em><sub>i<\/sub><\/em>), \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (5)<\/p><p>unde <em>s<\/em><em><sub>i<\/sub><\/em> este o stare ascuns\u0103 a RNN la momentul i, compus\u0103 din:<\/p><p><em>s<\/em><em><sub>i<\/sub><\/em><em> = f<\/em>(<em>s<\/em><em><sub>i<\/sub><\/em><sub>\u22121<\/sub>, <em>y<\/em><em><sub>i<\/sub><\/em><sub>-1<\/sub>, <em>c<\/em><em><sub>i<\/sub><\/em>). \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (6)<\/p><p>Diferen\u021ba fa\u021b\u0103 de ecua\u021bia (3) a unui codor-decodor clasic este c\u0103 aici probabilitatea este condi\u021bionat\u0103 de un vector de context distinct <em>c<\/em><em><sub>i<\/sub><\/em> pentru fiecare cuv\u00e2nt \u021bint\u0103 <em>y<\/em><em><sub>i<\/sub><\/em>. Vectorul de context <em>c<\/em><em><sub>i<\/sub><\/em> depinde de o serie de adnot\u0103ri (<em>h<\/em><sub>1<\/sub>, \u00b7 \u00b7 \u00b7 , <em>h<\/em><em><sub>Tx<\/sub><\/em>) c\u0103rora encoderul mapeaz\u0103 fraza de intrare. Astfel, <em>c<\/em><em><sub>i<\/sub><\/em> se calculeaz\u0103 ca o sum\u0103 ponderat\u0103 a acestor adnot\u0103ri <em>h<\/em><sub>i<\/sub>:<\/p><p><em>C<\/em><em><sub>i<\/sub><\/em> = SUMA, cu <em>j<\/em> de la 1 la <em>T<\/em><em><sub>x<\/sub><\/em>, din\u00a0 <em>\u03b1<\/em><em><sub>ij <\/sub><\/em><em>h<\/em><em><sub>j<\/sub><\/em> \u00a0 \u00a0 (7)<\/p><p>iar ponderea <em>\u03b1<\/em><em><sub>ij <\/sub><\/em>\u00a0a fiec\u0103rei adnot\u0103ri <em>h<\/em><em><sub>j <\/sub><\/em>e calculat\u0103 ca:<\/p><p><em>\u03b1<\/em><em><sub>ij<\/sub><\/em> = exp(<em>e<\/em><em><sub>ij <\/sub><\/em>) \/ SUMA, cu <em>k<\/em> de la 1 la <em>Tx<\/em>, din exp(<em>e<\/em><em><sub>ik<\/sub><\/em>), (8)<\/p><p>unde:<\/p><p><em>e<\/em><em><sub>ij <\/sub><\/em>\u00a0= a(<em>s<\/em><em><sub>i<\/sub><\/em><sub>-1<\/sub> , <em>h<\/em><em><sub>j<\/sub><\/em>).\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (9)<\/p><p>este un model de aliniere, care apreciaz\u0103 c\u00e2t de bine se aliniaz\u0103 ie\u0219irea din pozi\u021bia <em>j<\/em> cu intrarea din pozi\u021bia <em>i<\/em>. \u00cen aceast\u0103 formul\u0103 <em>s<\/em><em><sub>i<\/sub><\/em><sub>-1<\/sub> reprezint\u0103 starea ascuns\u0103 RNN de dinainte de emiterea lui <em>y<\/em><em><sub>i<\/sub><\/em>.<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00cen felul acesta decodorul decide asupra unor p\u0103r\u021bi din fraza surs\u0103 c\u0103rora s\u0103 le dea o importan\u021b\u0103 mai mare, iar codorul este degrevat de sarcina de a coda toat\u0103 informa\u021bia con\u021binut\u0103 \u00een fraza surs\u0103 \u00eentr-un vector de lungime constant\u0103.<\/p><p>\u00a0<\/p><p>CODORUL<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Re\u021beaua neuronal\u0103 descris\u0103 \u00een ecua\u021bia (1) cite\u0219te o secven\u021b\u0103 de intrare x \u00een ordine, de la primul (<em>x<\/em><sub>1<\/sub>) la ultimul simbol (<em>x<\/em><em><sub>Tx<\/sub><\/em>). Pentru a se lua \u00een considerare nu numai cuv\u00e2ntul precedent, dar \u0219i cel care urmeaz\u0103, autorii propun utilizarea unei re\u021bele bidirec\u021bionale (BiRNN, ca \u00een Schuster and Paliwal, 1997). Ea e compus\u0103 dintr-o re\u021bea neuronal\u0103 \u201c\u00eenainte\u201d, care cite\u0219te secven\u021ba de intrare (de la <em>x<\/em><sub>1<\/sub> la <em>x<\/em><em><sub>Tx<\/sub><\/em>) \u0219i calculeaz\u0103 secven\u021ba de st\u0103ri ascunse \u201cspre \u00eenainte\u201d (<em>forward hidden states<\/em>) , \u0219i o re\u021bea neuronal\u0103 \u201c\u00eenapoi\u201d, care cite\u0219te \u00een sens invers secven\u021ba de intrare (de la <em>x<\/em><em><sub>Tx<\/sub><\/em> la <em>x<\/em><sub>1<\/sub>) \u0219i calculeaz\u0103 secven\u021ba de st\u0103ri ascunse \u201cspre \u00eenapoi\u201d (<em>backward hidden states<\/em>) . Se ob\u021bine astfel o adnotare pentru fiecare cuv\u00e2nt <em>x<\/em><sub>j<\/sub> prin concatenarea st\u0103rii ascunse \u201c\u00eenainte\u201d cu cea \u201c\u00eenapoi\u201d , adic\u0103 <em>h<\/em><em><sub>j<\/sub><\/em> = . Astfel, adnot\u0103rile ascunse <em>h<\/em><em><sub>j<\/sub><\/em>\u00a0con\u021bin \u00een rezumat at\u00e2t cuvintele anterioare c\u00e2t \u0219i pe cele ulterioare lui <em>x<\/em><em><sub>j<\/sub><\/em> . Aceast\u0103 secven\u021b\u0103 de adnot\u0103ri este apoi folosit\u0103 de decodor pentru a compune vectorul context &#8211; ec. (7) \u0219i (8).<img decoding=\"async\" src=\"https:\/\/lh3.googleusercontent.com\/hI2_f7x55nVP-z5M8_hiulnaDmUuf68uUI8XzxWHnTmuKoMgKDRW2yKxUgLHG_JlQt8xCO-U23he95rki2u-WE24n7vb8mknTnA3HS9GGi86ypMrjlKNm1RWPfbouF97dLiJIE8\" \/><\/p><p>Figura 1 (\u00eemprumutat\u0103 din [Bahdanau et al., 2015]) sugereaz\u0103 c\u0103 pentru generarea unui cuv\u00e2nt pe canalul de ie\u0219ire nu numai c\u0103 se \u021bine seama de contextul intr\u0103rii pentru toat\u0103 fraza curent\u0103, dar anumite cuvinte pot c\u0103p\u0103ta ponderi mai mari dec\u00e2t celelalte. Acesta este \u00eensu\u0219i esen\u021ba mecanismului de aten\u021bie.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li><a href=\"https:\/\/arxiv.org\/search\/cs?searchtype=author&amp;query=Bahdanau%2C+D\">Dzmitry Bahdanau<\/a>,<a href=\"https:\/\/arxiv.org\/search\/cs?searchtype=author&amp;query=Cho%2C+K\"> Kyunghyun Cho<\/a>,<a href=\"https:\/\/arxiv.org\/search\/cs?searchtype=author&amp;query=Bengio%2C+Y\"> Yoshua Bengio<\/a> (2015): Neural Machine Translation by Jointly Learning to Align and Translate, ICLR, online at:<a href=\"https:\/\/arxiv.org\/pdf\/1409.0473.pdf\"><u>https:\/\/arxiv.org\/pdf\/1409.0473.pdf<\/u><\/a><\/li><li>Prodip Hore, Sayan Chatterjee (2019): A Comprehensive Guide to Attention Mechanism in Deep Learning for Everyone,<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2019\/11\/comprehensive-guide-attention-mechanism-deep-learning\/\"> <u>https:\/\/www.analyticsvidhya.com\/blog\/2019\/11\/comprehensive-guide-attention-mechanism-deep-learning\/<\/u><\/a> Tutorial online.<\/li><li>\u00a0\u200b\u200bSchuster, M. and Paliwal, K. K. (1997). Bidirectional recurrent neural networks. Signal Processing, IEEE Transactions on, 45(11), 2673\u20132681.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250619\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250619\" data-tab-index=\"19\" style=\"--n-tabs-title-order: 19;\" class=\" elementor-element elementor-element-18bb193 e-con-full e-flex e-con e-child\" data-id=\"18bb193\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-4d5d40c e-flex e-con-boxed e-con e-child\" data-id=\"4d5d40c\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b8dc172 elementor-widget elementor-widget-text-editor\" data-id=\"b8dc172\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">RoVG &#8211; Romanian Verbal Group Tagger<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Un set de reguli de parsare a grupurilor verbale. Abordare simbolic\u0103.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Curteanu, Neculai; Mihai Moruz; Diana Trandab\u0103\u0163; Cecilia Bolea; Iustin Dornescu, (2006): <em>The Structure and Parsing of Romanian Verbal Group and Predicate,<\/em> Proceedings of the ECIT2006 &#8211; 4th European Conference on Intelligent Systems and Technologies, Iasi, Romania, Septembrie 21-23, 2006, pp. 93-105.<\/li><li>Curteanu, N., D. Trandab\u0103\u0163, M. Moruz (2006): <em>Structura grupului verbal, predica\u0163ia lexical\u0103 \u015fi reprezentarea logic\u0103 a predicatului \u00een limba rom\u00e2n\u0103.<\/em> In Lucr\u0103rile atelierului RESURSE LINGVISTICE \u015eI INSTRUMENTE PENTRU PRELUCAREA LIMBII ROM\u00c2NE, (Ed. C. Ror\u0103scu, D. Tufi\u015f, D. Cristea), Editura Univ. &#8220;Al.I. Cuza&#8221; Ia\u015fi, ISBN: 978-973-703-208-9, pp. 143-148.\u00a0<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250620\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250620\" data-tab-index=\"20\" style=\"--n-tabs-title-order: 20;\" class=\" elementor-element elementor-element-e473762 e-con-full e-flex e-con e-child\" data-id=\"e473762\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-008f1ca e-flex e-con-boxed e-con e-child\" data-id=\"008f1ca\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-843bfc8 elementor-widget elementor-widget-text-editor\" data-id=\"843bfc8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">eDTLR extraction software<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Creat special pentru DR+DLR. Poate fi generalizat pentru digitizarea altor tipuri de dic\u021bionare.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Alex Moruz, Andrei Scutelnicu, Dan Cristea (2018). Interlinking and Extending Large Lexical Resources for Romanian, in V.P\u0103i\u0219, D.G\u00eefu, D.Trandab\u0103\u021b, D.Cristea, D.Tufi\u0219 (eds.) Proceedings of the 13th International Conference on Linguistic Resources and Tools for Processing Romanian Language, noiembrie 22-23, Editura Universit\u0103\u021bii \u201cA.I.Cuza\u201d din Ia\u0219i, pp. 125-132, ISSN 1843-911X, link: <a href=\"https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/paper%2014.pdf\"><u>https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/paper%2014.pdf<\/u><\/a>\u00a0<\/li><li>Mihai Alex Moruz, Dan Cristea (2016). A Bootstrapping System for Dictionary Management and Parsing, \u00een M. Mitrofan, D. G\u00eefu, D. Tufi\u0219, D. Cristea (eds.): Proceedings of the 12th International Conference on Linguistic Resources And Tools For Processing The Romanian Language \u2013 ConsILR, M\u0103lini, 27-29 october 2016, \u201eAlexandru Ioan Cuza\u201d University Publishing House, pages 153-162, ISSN 1843-911X, link: <a href=\"https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/Consilr2016_paper_3_final.pdf\"><u>https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/Consilr2016_paper_3_final.pdf<\/u><\/a>\u00a0<\/li><li>Dan Cristea, Gabriela Haja, Alex Moruz, Marius R\u0103schip, M\u0103d\u0103lin Ionel Patra\u0219cu (2011). Statistici par\u021biale la \u00eencheierea proiectului eDTLR \u2013 Dic\u021bionarul Tezaur al Limbii Rom\u00e2ne \u00een format electronic. \u00cen Rodica Zafiu, Camelia U\u0219urelu, Helga Bogdan Oprea (editori), Limba rom\u00e2n\u0103. Ipostaze ale varia\u021biei lingvistice. Actele celui de-al 10-lea Colocviu al Catedrei de limba rom\u00e2n\u0103 (Bucure\u015fti, 3-4 decembrie 2010), vol. I, Gramatic\u0103 \u015fi fonologie, lexic, semantic\u0103, terminologii, istoria limbii rom\u00e2ne, dialectologie \u015fi filologie, Bucure\u0219ti, Editura Universit\u0103\u021bii din Bucure\u0219ti, 2011, pp. 213-224, ISBN 978-606-16-0046-5, link: <a href=\"https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/Cristea_Haja_Moruz_Raschip_Patrascu.pdf\"><u>https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/Cristea_Haja_Moruz_Raschip_Patrascu.pdf<\/u><\/a>\u00a0<\/li><li>Dan Cristea, Marius R\u0103schip, Alex Moruz (2009). Steps in Building the Electronic Version of the Thesaurus Dictionary of the Romanian Language. In Proceedings of the IVth National Conference The Academic Days of the Academy of Technical Science of Romania, ASTR &#8211; the Ia\u0219i branch and &#8220;Gheorghe Asachi&#8221; Tehnical University Ia\u0219i, Agir Publishing House, ISSN 2006-6586, link: <a href=\"https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/ASTR09_CristeaRaschipMoruz.pdf\"><u>https:\/\/profs.info.uaic.ro\/~dcristea\/papers\/ASTR09_CristeaRaschipMoruz.pdf<\/u><\/a><\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250621\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250621\" data-tab-index=\"21\" style=\"--n-tabs-title-order: 21;\" class=\" elementor-element elementor-element-d862cd8 e-con-full e-flex e-con e-child\" data-id=\"d862cd8\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-3c0e2d5 e-flex e-con-boxed e-con e-child\" data-id=\"3c0e2d5\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-93d488c elementor-widget elementor-widget-text-editor\" data-id=\"93d488c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Colec\u021bie de API-uri de acces \u00een RoWordNet (RoWN &#8211; Romanian WordNet)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>WordNet-ul rom\u00e2nesc este creat dup\u0103 modelul Princeton WordNet (Fellbaum, 1998), care a produs o adev\u0103rat\u0103 revolu\u021bie \u00een domeniul lingvisticii computa\u021bionale prin dezvolt\u0103rile \u0219tiin\u021bifice \u0219i tehnologice pe care le-a generat de la crearea lui. O baz\u0103 de date de tip wordnet este o colec\u021bie de substantive, verbe, adjective \u0219i adverbe, ce poate fi privit\u0103 ca o re\u021bea constituit\u0103 din noduri unde se reg\u0103sesc cuvinte care, \u00een anumite contexte, pot avea acela\u0219i \u00een\u021beles. Cuvintele (numite lexicali) sunt grupate \u00een seturi sinonimice numite synset-uri, fiecare exprim\u00e2nd un concept lingvistic distinct, reprezent\u00e2nd noduri ale re\u021belei. API-urile din aceast\u0103 colec\u021bie realizeaz\u0103 diferite opera\u021bii de acces \u00een structura RoWN.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Tufi\u015f, D., <em>Ro-WordNet: ontologie lexical\u0103 pentru limba rom\u00e2n\u0103<\/em>, Academica, XVIII (208\u2013209): 30\u201334, februarie-martie 2008. ISSN 1220\u20135737.<\/li><li>Tufi\u015f, D., Barbu E., Barbu Mititelu V., Ion R., Bozianu L., The Romanian Wordnet, Romanian Journal of Information Science and Technology, 7, 1\u20132, 2004, pp. 107\u2013124.<\/li><li>Barbu Mititelu, V., Re\u0163ea semantico-deriva\u0163ional\u0103 pentru limba rom\u00e2n\u0103, Edit. Muzeul Literaturii Rom\u00e2ne, Bucure\u015fti, 2013.<\/li><li>Baccianella, S., Andrea E., Fabrizio S. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining., LREC, 10, 2010.<\/li><li>Tufi\u015f, D., Barbu Mititelu, V., \u015etef\u0103nescu, D., Ion, R., The Romanian Wordnet in a Nutshell, Language Resources and Evaluation, X, 10, 2013.<\/li><li>Niles, I., Pease, A., Towards a standard upper ontology, Proceedings of the International Conference on Formal Ontology in Information Systems, 2001.<\/li><li>Resnik, P., Using information content to evaluate semantic similarity in a taxonomy, 14th International Joint Conference on Artificial Intelligence, Montreal, 1995.<\/li><li>Lesk, M. (1986), Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone, 5th SIGDOC, New York, pp. 24\u201326.<\/li><li>Banerjee S., Pedersen T. Extended gloss overlaps as a measure of semantic relatedness, 2003.<\/li><li>Boro\u015f, T., \u015etef\u0103nescu, D., Ion, R. Handling Two Difficult Challenges for Text-to-Speech Synthesis Systems: Out-ofVocabulary Words and Prosody: A Case Study in Romanian, Where Humans Meet Machines, Springer New York, 2013, pp. 137\u2013161.<\/li><li>Ion, R., Tufi\u015f, D., Multilingual Word Sense Disambiguation Using Aligned Wordnets, Romanian Journal on Information Science and Technology, Special Issue on BalkaNet, 7, pp. 183\u2013200, 2004.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250622\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250622\" data-tab-index=\"22\" style=\"--n-tabs-title-order: 22;\" class=\" elementor-element elementor-element-125f965 e-con-full e-flex e-con e-child\" data-id=\"125f965\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-66710ca e-flex e-con-boxed e-con e-child\" data-id=\"66710ca\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-401ccb6 elementor-widget elementor-widget-text-editor\" data-id=\"401ccb6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Keras<\/h3><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Bibliotec\u0103 Python pentru \u00eenv\u0103\u021barea automat\u0103, ce pune la dispozi\u021bia\u00a0 utilizatorului un set de module care abstractizeaz\u0103\/unific\u0103 procesul de antrenare a modelelor de \u00eenv\u0103\u021bare automat\u0103 indiferent de platforma pe care se produce antrenarea propriu-zis\u0103 (TensorFlow, CNTK etc.).<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><p>Chollet Francois et. al. (2015), <em>Keras<\/em>.\u00a0https:\/\/keras.io\/<\/p><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250623\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250623\" data-tab-index=\"23\" style=\"--n-tabs-title-order: 23;\" class=\" elementor-element elementor-element-238c5f9 e-con-full e-flex e-con e-child\" data-id=\"238c5f9\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-d6be1f2 e-flex e-con-boxed e-con e-child\" data-id=\"d6be1f2\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-96981b8 elementor-widget elementor-widget-text-editor\" data-id=\"96981b8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">YOLO (You only look once)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p>Descriere: YOLO este un algoritm specializat pe detec\u021bia \u0219i recunoa\u0219terea de obiecte \u00een imagini. A ap\u0103rut prima oar\u0103 \u00een 2016, fiind creat de c\u0103tre Joseph Redmon, iar de-a lungul timpului a fost eficientizat \u00eentr-o manier\u0103 iterativ\u0103, ajung\u00e2nd la versiunea 5.<\/p><p>YOLO folose\u0219te deep learning pentru a \u00eenv\u0103\u021ba reprezent\u0103rile obiectelor \u00een imagine. Modelul este format din mai multe re\u021bele neuronale specializate pe \u00een\u021belegerea imaginilor (VGG, ResNet, MobileNet etc), detec\u021bia obiectelor prin trasarea unor dreptunghiuri peste fiecare obiect \u0219i \u00een final o re\u021bea care realizeaz\u0103 clasificarea imaginilor din dreptunghiuri.<\/p><p>YOLOv5 prezint\u0103 mai multe arhitecturi (small, medium, large, xlarge). Acestea pot fi folosite \u00een func\u021bie de problema pe care dorim sa o rezolvam. Dimensiunea arhitecturilor variaz\u0103 de la 15 MB la 170 MB.<\/p><p>\u00a0<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>Redmon, J. ,\u00a0 Divvala, S. ,\u00a0 Girshick, R. , &amp;\u00a0 Farhadi, A. . (2016). You only look once: unified, real-time object detection.<\/li><li>\u00a0Bochkovskiy, A. ,\u00a0 Wang, C. Y. , &amp;\u00a0 Liao, H. . (2020). Yolov4: optimal speed and accuracy of object detection.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250624\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250624\" data-tab-index=\"24\" style=\"--n-tabs-title-order: 24;\" class=\" elementor-element elementor-element-932de4e e-con-full e-flex e-con e-child\" data-id=\"932de4e\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-36c58e3 e-flex e-con-boxed e-con e-child\" data-id=\"36c58e3\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-de342be elementor-widget elementor-widget-text-editor\" data-id=\"de342be\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Word embeddings<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p><em>Word Embeddings<\/em> este una dintre cele mai populare reprezent\u0103ri ale vocabularului. Modelul este capabil s\u0103 capteze contextul unui cuv\u00e2nt \u00eentr-un document, similaritatea semantic\u0103 \u0219i sintactic\u0103 a cuvintelor, rela\u021bia cu alte cuvinte etc., utiliz\u00e2nd pentru aceasta reprezent\u0103ri vectoriale.<\/p><p>\u00a0\u00a0\u00a0\u00a0 S\u0103 lu\u0103m ca exemplu urm\u0103toarele fraze similare: \u201cAi o zi bun\u0103\u201d \u0219i \u201cAi o zi minunat\u0103\u201d. Ele au aproximativ acela\u0219i sens, iar dac\u0103 construim un vocabular exhaustiv (s\u0103 \u00eel numim <em>V<\/em>), acesta ar fi <em>V<\/em> = {Ai, o, zi, bun\u0103, minunat\u0103}.<\/p><p>\u00a0\u00a0\u00a0\u00a0 Acum, s\u0103 cre\u0103m un vector codificat pentru fiecare dintre aceste cuvinte din <em>V<\/em>. Lungimea vectorului nostru va fi egal\u0103 cu dimensiunea lui <em>V<\/em> (= 5). Dac\u0103 vectorul este lung, multe valori vor fi zerouri, nenule fiind doar elementele de la indec\u0219ii ce reprezint\u0103 cuvintele din vocabular ce se g\u0103sesc \u0219i \u00een contextul cuv\u00e2ntului dat.<\/p><p>Putem s\u0103 vizualiz\u0103m aceste codific\u0103ri \u00eentr-un spa\u021biu de 5 dimensiuni, \u00een care fiecare cuv\u00e2nt ocup\u0103 una dintre dimensiuni \u0219i nu are nimic de-a face cu restul (nici o proiec\u021bie de-a lungul celorlalte dimensiuni). Acest lucru \u00eenseamn\u0103 c\u0103 &#8220;bun\u0103&#8221; \u0219i &#8220;minunat\u0103&#8221; sunt la fel de diferite ca &#8220;ziua&#8221; \u0219i &#8220;ai&#8221;, ceea ce, de fapt, nu este adev\u0103rat. De aici ideea de a genera reprezent\u0103ri distribuite. Intuitiv, introducem o dependen\u021b\u0103 a unui cuv\u00e2nt de celelalte cuvinte.\u00a0<\/p><p>Tipul resursei: model.<\/p><p>Scop: identificarea similarit\u0103\u021bii semantice \u0219i sintactice a cuvintelor, rela\u021bia cu alte cuvinte.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><p>\u00a0<\/p><ul><li>Word embeddings, <u><a href=\"https:\/\/cbail.github.io\/textasdata\/word2vec\/rmarkdown\/word2vec.html\">https:\/\/cbail.github.io\/textasdata\/word2vec\/rmarkdown\/word2vec.html<\/a><\/u><\/li><li>Mikolov Tomas, Sutskever Ilya, Chen Kai, Corrado Greg, Dean, Jeffrey (2013), <em>Distributed Representations of Words and Phrases and their Compositionality<\/em>, <a href=\"https:\/\/arxiv.org\/abs\/1310.4546\">\u00a0<u>https:\/\/arxiv.org\/abs\/1310.4546<\/u><\/a><\/li><li>Levy Omer, Goldberg Yoav (2014),<a href=\"https:\/\/levyomer.files.wordpress.com\/2014\/04\/linguistic-regularities-in-sparse-and-explicit-word-representations-conll-2014.pdf\"> <em>Linguistic Regularities in Sparse and Explicit Word\u00a0 Representations<\/em><\/a><\/li><li><u>https:\/\/towardsdatascience.com\/introduction-to-word-embedding-and-word2vec-652d0c2060fa, <\/u><\/li><li><u>https:\/\/towardsdatascience.com\/what-the-heck-is-word-embedding-b30f67f01c81<\/u><\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-59459250625\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-59459250625\" data-tab-index=\"25\" style=\"--n-tabs-title-order: 25;\" class=\" elementor-element elementor-element-17e453e e-con-full e-flex e-con e-child\" data-id=\"17e453e\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-af1fcc4 e-flex e-con-boxed e-con e-child\" data-id=\"af1fcc4\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-898f44b elementor-widget elementor-widget-text-editor\" data-id=\"898f44b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"display-4\">Convolutional neural networks (CNN)<\/h3><div class=\"row\"><div class=\"row item\"><div class=\"col-2\">Descriere<\/div><div class=\"col-7\"><p><strong><em>Convolutional neural networks <\/em><\/strong>(CNN) sunt un tip de re\u021bele neuronale artificiale\u00a0 utilizate \u00een principal pentru problemele legate de interpretarea datelor vizuale (imagini\u00a0 \u0219i videoclipuri), localizarea obiectelor, segmentarea semantic\u0103, recunoa\u0219terea optic\u0103 a\u00a0 caracterelor etc. Un CNN poate fi construit cu mai multe straturi. Se cunosc trei mari\u00a0 categorii de straturi: de <em>convolu\u021bie<\/em>, de <em>pooling <\/em>\u0219i <em>complet conectate<\/em>, asociate cu o\u00a0 func\u021bie comun\u0103 de activare.\u00a0\u00a0<\/p><p><em>Stratul de convolu\u021bie <\/em>este blocul central al CNN-ului \u0219i reprezint\u0103 partea principal\u0103\u00a0 a sarcinii computa\u021bionale a re\u021belei. Parametrii stratului convolu\u021bional constau \u00eentr-un set\u00a0 de filtre aplicate. Fiecare filtru este de mici dimensiuni (de-a lungul l\u0103\u021bimii \u0219i \u00een\u0103l\u021bimii),\u00a0 dar se extinde prin toat\u0103 ad\u00e2ncimea volumului de intrare.\u00a0\u00a0<\/p><p><em>Stratul de pooling <\/em>se introduce periodic \u00eentre straturile consecutive convolu\u021bionale\u00a0 \u00eentr-o arhitectur\u0103 CNN. Func\u021bia sa este de a reduce progresiv dimensiunea spa\u021bial\u0103,\u00a0 pentru a reduce cantitatea de parametri \u0219i de calcul \u00een re\u021bea \u0219i, prin urmare, de a evita\u00a0 supraantrenarea. Stratul \u201ePooling\u201d func\u021bioneaz\u0103 independent la fiecare strat din\u00a0 ad\u00e2ncimea intr\u0103rii \u0219i o redimensioneaz\u0103 spa\u021bial, utiliz\u00e2nd opera\u021bia MAX.\u00a0<\/p><p><em>Stratul complet conectat <\/em>are neuronii cu conexiuni complete la toate activ\u0103rile din\u00a0 stratul anterior, a\u0219a cum se observ\u0103 \u00een re\u021belele neuronale obi\u0219nuite, Artificial Neural\u00a0 Networks (ANN). Activarea lor poate fi astfel calculat\u0103 cu o multiplicare a matricei\u00a0 ad\u0103ug\u00e2ndu-se \u0219i deplasarea (termenul prag), cunoscut drept bias.\u00a0\u00a0<\/p><p>Clasificarea poate fi f\u0103cut\u0103 de un strat Softmax. Practic, fiecare <em>neuron <\/em>dintr-un\u00a0 strat este conectat la fiecare <em>neuron <\/em>din stratul urm\u0103tor, iar fiecare <em>strat <\/em>\u00ee\u0219i prime\u0219te\u00a0 aportul din ie\u0219irea <em>stratului <\/em>anterior.\u00a0<\/p><p>Mai jos sunt date c\u00e2teva exemple de aplica\u021bii ale CNN.\u00a0\u00a0<\/p><p>I. <em>\u00cen sectorul s\u0103n\u0103t\u0103\u021bii<\/em>, aplica\u021biile de imagistic\u0103 medical\u0103 implic\u0103 clasificarea,\u00a0 detectarea \u0219i segmentarea obiectelor. Visual Geometry Group (VGGNet) este o\u00a0 re\u021bea dezvoltat\u0103 de Karen Simonyan \u0219i Andrew Zisserman (Simonyan and\u00a0 Zisserman, 2015). Rezultatele lor arat\u0103 c\u0103 ad\u00e2ncimea re\u021belei are o influen\u021b\u0103\u00a0 semnificativ\u0103 asupra performan\u021bei re\u021belei. Modelul lor con\u021bine 16 straturi\u00a0 convolu\u021bionale complet conectate \u0219i o arhitectur\u0103 omogen\u0103 care realizeaz\u0103\u00a0 convolu\u021bii de tip 3&#215;3 \u0219i pooling 2&#215;2.\u00a0<\/p><p>II. <em>\u00cen sectorul economic<\/em>, unde se \u00eencadreaz\u0103 \u0219i lucrarea amintit\u0103, a fost utilizat\u00a0 <strong><em>word2vec <\/em><\/strong>ca \u00eencorporare de cuvinte pentru a forma stratul de intrare al CNN-ului. <strong><em>Word2vec <\/em><\/strong>este utilizat pentru a construi matricea vectorial\u0103 ce\u00a0 caracterizeaz\u0103 d.p.d.v. semantic fiecare cuv\u00e2nt din intrare. \u00cen timp ce se afl\u0103 \u00een\u00a0 stratul de mapare, mai multe h\u0103r\u021bi cu caracteristici alc\u0103tuiesc unul dintre\u00a0 straturile de calcul; o hart\u0103 caracteristic\u0103 corespunde unui plan \u0219i toate ponderile\u00a0 neuronilor de pe acela\u0219i plan sunt egale. Designul CNN l-a f\u0103cut un clasificator\u00a0 adecvat pentru extragerea caracteristicilor semantice din textele corporale.\u00a0 Descrierea detaliat\u0103 a structurii convolu\u021bionale a modelului de re\u021bea neuronal\u0103 se\u00a0 afl\u0103 \u00een lucrarea ata\u0219at\u0103 (Li et al., 2020).\u00a0<\/p><p>III. <em>\u00cen sectorul comunic\u0103rii virtuale<\/em>, unde se \u00eencadreaz\u0103 lucrarea (Iftene et al., 2020), modelul CNN este utilizat pentru analiza \u00een timp real a canalului\u00a0 Twitter, dovedindu-se c\u0103 el poate s\u0103 ofere elemente cheie despre credibilitatea\u00a0 tweet-urilor c\u00e2t \u0219i a utilizatorilor care le-au postat. \u201cH\u0103r\u021bile\u201d utilizate aici\u00a0 integreaz\u0103 informa\u021bii culese de la utilizatori. Se creaz\u0103 astfel \u201cimagini\u201d \u00een care se\u00a0 pot apoi localiza \u201czonele\u201d, adic\u0103 post\u0103rile \u0219i autorii lor, din care provin \u0219tirile\u00a0 categorizate \u00een <em>false <\/em>\u0219i <em>non-false<\/em>.<\/p><\/div><\/div><div class=\"row item\"><div class=\"col-2\">Referin\u021be<\/div><div class=\"col-7\"><ul><li>(Simonyan and\u00a0 Zisserman, 2015) Simonyan, Karen and Zisserman, Andrew (2015). <em>Very Deep Convolutional Networks\u00a0 for Large-Scale Image Recognition<\/em>. In: International Conference on Learning\u00a0 Representations &#8211; arXiv:1409.1556.<\/li><li>(Li et al., 2020)\u00a0Youzhu Li, Huiling Zhou, Zhonglong Lin, Yifan Wang, Shunjie Chen, Chang Liu, Zhouyang Wang,\u00a0Daniela Gifu,\u00a0Jingbo Xia.\u00a0<em>Investigation in the influences of public opinion indicators on vegetable prices by corpora construction and WeChat article analysis<\/em>. In:\u00a0Future Generation Computer Systems, vol. 102, pages 876-888, 2020,\u00a0https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X18327341<\/li><li>(Iftene et al., 2020) Adrian Iftene, Daniela G\u00eefu, Andrei-Remus Miron, Mihai-\u0218tefan Dudu.\u00a0<em>A Real-Time System for Credibility on Twitter.\u00a0<\/em>In:\u00a0Proceedings of the 12th Language Resources and Evaluation Conference, pages 6166\u20136173, Marseille, France. European Language Resources Association,\u00a0https:\/\/aclanthology.org\/2020.lrec-1.757.pdf<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>LDA SVM NLTK Graph networks NLP-Cube GloVe embeddings ELMo embeddings Character-level embeddings TensorFlow Seq2seq Long [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-10","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/pages\/10","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/comments?post=10"}],"version-history":[{"count":20,"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/pages\/10\/revisions"}],"predecessor-version":[{"id":159,"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/pages\/10\/revisions\/159"}],"wp:attachment":[{"href":"https:\/\/lsplr.iit.academiaromana-is.ro\/index.php\/wp-json\/wp\/v2\/media?parent=10"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}