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2014 - New Directions in Vector Space Models of Meaning.pdf", "accessDate": "2014-10-13T07:52:24Z", "url": "http://www.cs.ox.ac.uk/files/6605/aclVectorTutorial.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Blunsom et al. - 2014 - New Directions in Vector Space Models of Meaning.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2014-10-13T07:52:24Z", "dateModified": "2014-10-13T07:52:40Z" } }, { "key": "6B2TC8UE", "version": 42, "library": { "type": "group", "id": 280697, "name": "deep_learning_nlp", "links": { "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/280697/items/6B2TC8UE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp/items/6B2TC8UE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 748919, "username": "renaudr", "name": "Renaud Richardet", "links": { "alternate": { "href": "https://www.zotero.org/renaudr", "type": "text/html" } } }, "creatorSummary": "Blunsom et al.", "parsedDate": "2014", "numChildren": 2 }, "data": { "key": "6B2TC8UE", "version": 42, "itemType": "journalArticle", "title": "New Directions in Vector Space Models of Meaning", "creators": [ { "creatorType": "author", "firstName": "Phil", "lastName": "Blunsom" }, { "creatorType": "author", "firstName": "Edward", "lastName": "Grefenstette" }, { "creatorType": "author", "firstName": "Karl Moritz", "lastName": "Hermann" } ], "abstractNote": "Symbolic approaches have dominated NLP as a means to model syntactic and semantic aspects of natural language. While powerful inferential tools exist for such models, they suffer from an inability to capture correlation between words and to provide a continuous model for word, phrase, and document similarity. Distributed representations are one mechanism to overcome these constraints. This tutorial will supply NLP researchers with the mathematical and conceptual background to make use of vector-based models of meaning in their own research. We will begin by motivating the need for a transition from symbolic representations to distributed ones. We will briefly cover how collocational (distributional) vectors can be used and manipulated to model word meaning. We will discuss the progress from distributional to distributed representations, and how neural networks allow us to learn word vectors and condition them on metadata such as parallel texts, topic labels, or sentiment labels. Finally, we will present various forms of semantic vector composition, and discuss their relative strengths and weaknesses, and their application to problems such as language modelling, paraphrasing, machine translation and document classification. This tutorial aims to bring researchers up to speed with recent developments in this fast-moving field. It aims to strike a balance between providing a general introduction to vector-based models of meaning, an analysis of diverging strands of research in the field, and also being a hands-on tutorial to equip NLP researchers with the necessary tools and background knowledge to start working on such models. Attendees should be comfortable with basic probability, linear algebra, and continuous mathematics. 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A systematic comparison of c.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2014-10-13T07:51:01Z", "dateModified": "2014-10-13T07:51:08Z" } }, { "key": "EKAF4JNT", "version": 41, "library": { "type": "group", "id": 280697, "name": "deep_learning_nlp", "links": { "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/280697/items/EKAF4JNT", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp/items/EKAF4JNT", "type": "text/html" } }, "meta": { "createdByUser": { "id": 748919, "username": "renaudr", "name": "Renaud Richardet", "links": { "alternate": { "href": "https://www.zotero.org/renaudr", "type": "text/html" } } }, "creatorSummary": "Baroni et al.", "parsedDate": "2014", "numChildren": 1 }, "data": { "key": "EKAF4JNT", "version": 41, "itemType": "conferencePaper", "title": "Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors", "creators": [ { "creatorType": "author", "firstName": "Marco", "lastName": "Baroni" }, { "creatorType": "author", "firstName": "Georgiana", "lastName": "Dinu" }, { "creatorType": "author", "firstName": "Germán", "lastName": "Kruszewski" } ], "abstractNote": "", "proceedingsTitle": "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics", "conferenceName": "", "publisher": "", "place": "", "date": "2014", "eventPlace": "", "volume": "1", "issue": "", "numberOfVolumes": "", "pages": "", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "http://clic.cimec.unitn.it/marco/publications/acl2014/baroni-etal-countpredict-acl2014.pdf", "accessDate": "2014-10-13T07:51:01Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "3QPWT3GR" ], "relations": {}, "dateAdded": "2014-10-13T07:51:01Z", "dateModified": "2014-10-13T07:51:01Z" } }, { "key": "9ZFM9D6P", "version": 40, "library": { "type": "group", "id": 280697, "name": "deep_learning_nlp", "links": { "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/280697/items/9ZFM9D6P", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp/items/9ZFM9D6P", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/280697/items/5GHXFGDA", "type": "application/json" } }, "meta": { "createdByUser": { "id": 748919, "username": "renaudr", "name": "Renaud Richardet", "links": { "alternate": { "href": "https://www.zotero.org/renaudr", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "9ZFM9D6P", "version": 40, "parentItem": "5GHXFGDA", "itemType": "attachment", "linkMode": "imported_url", "title": "JeffreyPennington and Manning - 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2014 - word2vec Explained deriving Mikolov et al.'s nega.pdf", "accessDate": "2014-10-12T21:23:17Z", "url": "http://www.arxiv.org/pdf/1402.3722.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Goldberg and Levy - 2014 - word2vec Explained deriving Mikolov et al.'s nega.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2014-10-12T21:23:17Z", "dateModified": "2014-10-12T21:23:23Z" } }, { "key": "7GJ82KAQ", "version": 38, "library": { "type": "group", "id": 280697, "name": "deep_learning_nlp", "links": { "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/280697/items/7GJ82KAQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp/items/7GJ82KAQ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 748919, "username": "renaudr", "name": "Renaud Richardet", "links": { "alternate": { "href": "https://www.zotero.org/renaudr", "type": "text/html" } } }, "creatorSummary": "Goldberg and Levy", "parsedDate": "2014-02-15", "numChildren": 2 }, "data": { "key": "7GJ82KAQ", "version": 38, "itemType": "journalArticle", "title": "word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method", "creators": [ { "creatorType": "author", "firstName": "Yoav", "lastName": "Goldberg" }, { "creatorType": "author", "firstName": "Omer", "lastName": "Levy" } ], "abstractNote": "The word2vec software of Tomas Mikolov and colleagues (https://code.google.com/p/word2vec/ ) has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers. We found the description of the models in these papers to be somewhat cryptic and hard to follow. While the motivations and presentation may be obvious to the neural-networks language-modeling crowd, we had to struggle quite a bit to figure out the rationale behind the equations. This note is an attempt to explain equation (4) (negative sampling) in \"Distributed Representations of Words and Phrases and their Compositionality\" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.", "publicationTitle": "arXiv:1402.3722 [cs, stat]", "publisher": "", "place": "", "date": "2014-02-15", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://arxiv.org/abs/1402.3722", "accessDate": "2014-10-12T21:23:17Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "word2vec Explained", "language": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "arXiv: 1402.3722", "tags": [ { "tag": "Computer Science - Computation and Language", "type": 1 }, { "tag": "Computer Science - Learning", "type": 1 }, { "tag": "Statistics - Machine Learning", "type": 1 } ], "collections": [ "3QPWT3GR" ], "relations": {}, "dateAdded": "2014-10-12T21:23:17Z", "dateModified": "2014-10-12T21:23:17Z" } }, { "key": "3TREVKAR", "version": 36, "library": { "type": "group", "id": 280697, "name": "deep_learning_nlp", "links": { "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/280697/items/3TREVKAR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp/items/3TREVKAR", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/280697/items/AC652X5W", "type": "application/json" } }, "meta": { "createdByUser": { "id": 748919, "username": "renaudr", "name": "Renaud Richardet", "links": { "alternate": { "href": "https://www.zotero.org/renaudr", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "3TREVKAR", "version": 36, "parentItem": "AC652X5W", "itemType": "attachment", "linkMode": "imported_url", "title": "Larochelle and Lauly - 2012 - A Neural Autoregressive Topic Model.pdf", "accessDate": "2014-10-03T10:06:33Z", "url": "http://papers.nips.cc/paper/4613-a-neural-autoregressive-topic-model.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Larochelle and Lauly - 2012 - A Neural Autoregressive Topic Model.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2014-10-03T10:06:33Z", "dateModified": "2014-10-03T10:06:42Z" } }, { "key": "AC652X5W", "version": 36, "library": { "type": "group", "id": 280697, "name": "deep_learning_nlp", "links": { "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/280697/items/AC652X5W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/deep_learning_nlp/items/AC652X5W", "type": "text/html" } }, "meta": { "createdByUser": { "id": 748919, "username": "renaudr", "name": "Renaud Richardet", "links": { "alternate": { "href": "https://www.zotero.org/renaudr", "type": "text/html" } } }, "creatorSummary": "Larochelle and Lauly", "parsedDate": "2012", "numChildren": 2 }, "data": { "key": "AC652X5W", "version": 36, "itemType": "bookSection", "title": "A Neural Autoregressive Topic Model", "creators": [ { "creatorType": "author", "firstName": "Hugo", "lastName": "Larochelle" }, { "creatorType": "author", "firstName": "Stanislas", "lastName": "Lauly" }, { "creatorType": "editor", "firstName": "F.", "lastName": "Pereira" }, { "creatorType": "editor", "firstName": "C. 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