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Word sense
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#	Item
111	Computing Text Semantic Relatedness using the Contents and Links of a Hypertext Encyclopedia: Extended Abstract∗ Majid Yazdani Idiap Research Institute and EPFL Centre du Parc, Rue MarconiMartigny, Switzerland Add to Reading List Source URL: majid.yazdani.me Language: English - Date: 2015-08-19 06:57:44 Computational linguistics Knowledge representation Networks Semantics Natural language processing Semantic similarity Semantic network Word-sense disambiguation Graph WordNet Topology Scale-free network
112	Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning Yulia Tsvetkov♠ Manaal Faruqui♠ Wang Ling♣ Brian MacWhinney♠ Chris Dyer♣♠ ♠ Carnegie Mellon University ♣ G Add to Reading List Source URL: www.aclweb.org Language: English - Date: 2016-08-01 10:37:36 Machine learning Computational linguistics Artificial neural networks Natural language processing Graphical models Word embedding Word-sense disambiguation Word2vec Conditional random field Named-entity recognition Curriculum Support vector machine
113	Microsoft Word - The Common Sense of Copyingtable and appendix correctiondoc Add to Reading List Source URL: nonpartisaneducation.org Language: English - Date: 2011-10-30 22:02:32 Mathematics education International Association for the Evaluation of Educational Achievement Trends in International Mathematics and Science Study Curriculum Education reform Education in Japan Elementary school Music education Education in the United States Secondary education in Japan
114	John Benjamins Publishing Company This is a contribution from International Journal of Corpus Linguistics 17:3 © 2012. John Benjamins Publishing Company This electronic file may not be altered in any way. The author(s) Add to Reading List Source URL: ucnk.ff.cuni.cz Language: English Computational linguistics Applied linguistics Corpus linguistics Discourse analysis Semantics Text corpus Parallel text Corpus manager Word-sense disambiguation International Corpus of English Concordance Corpus
115	Unsupervised Semantic Parsing Hoifung Poon Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA, U.S.A. Add to Reading List Source URL: twiki.di.uniroma1.it Language: English - Date: 2013-03-28 13:13:45 Computational linguistics Grammar Semantics Formal languages Natural language parsing Dependency grammar Parsing Argument Natural language processing Semantic role labeling Word-sense disambiguation Free variables and bound variables
116	N a t u r a l L a n g u a g e P r o c e s s i n g Add to Reading List Source URL: wwwusers.di.uniroma1.it Language: English - Date: 2003-01-22 10:22:18 Knowledge representation Ontology Computational linguistics Open data Knowledge engineering WordNet Semantic Web Formal ontology Semantic network Word-sense disambiguation Hyponymy and hypernymy Machine translation
117	DAY 2 Poster SessionsArea 1 Add to Reading List Source URL: lrec2016.lrec-conf.org Language: English - Date: 2016-04-20 05:52:21 Computational linguistics Corpus linguistics Applied linguistics Natural language processing Semantics Text corpus Treebank Word-sense disambiguation Terminology extraction Machine translation International Corpus of English FrameNet
118	Do Multi-Sense Embeddings Improve Natural Language Understanding? Dan Jurafsky Computer Science Department Stanford University Stanford, CA 94305, USA Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2015-10-31 15:44:36 Computational linguistics Semantics Natural language processing Lexical semantics Word-sense disambiguation Word embedding Word2vec Language model Semantic similarity SemEval Embedding Artificial neural network
119	Automatic Reduction of a Document-Derived Noun Vocabulary Add to Reading List Source URL: wayneyuwu.com Language: English - Date: 2016-07-13 12:24:42 Computational linguistics Lexical semantics Knowledge representation Semantics Hierarchy WordNet Word-sense disambiguation Hyponymy and hypernymy Semantic similarity Word sense Lexicon Taxonomy
120	Assignment 4 L445 / L545 / B659 Due Tuesday, March 1 1. Using your knowledge of n-grams, explain to a layperson what n-grams models are, what they do, what they don’t do well, and why smoothing is necessary. You should Add to Reading List Source URL: cl.indiana.edu Language: English - Date: 2016-02-18 19:43:03 Corpus linguistics Part-of-speech tagging Word-sense disambiguation Computational linguistics Brill tagger Treebank Tagger Constraint Grammar Visitor Based Network Tag

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