Word Sense Disambiguation: Algorithms and Applications

This is the first book to cover the entire topic of word sense disambiguation (WSD) including: all the major algorithms, techniques, performance measures, results, philosophical issues, and applications. Leading researchers in the field have contributed chapters that synthesize and provide an overview of past and state-of-the-art research across the field. The editors have carefully organized the chapters into sub-topics. Researchers and lecturers will learn about the full range of what has been done and where the field is headed. Developers will learn which technique(s) will apply to their particular application, how to build and evaluate systems, and what performance to expect. An accompanying Website provides links to resources for WSD and a searchable index of the book.

[1]  Cheng Niu,et al.  Word Independent Context Pair Classification Model for Word Sense Disambiguation , 2005, CoNLL.

[2]  Ted Pedersen,et al.  Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces , 2004, CoNLL.

[3]  Dragomir R. Radev,et al.  LexRank: Graph-based Centrality as Salience in Text Summarization , 2004 .

[4]  George A. Miller,et al.  A Semantic Concordance , 1993, HLT.

[5]  Paola Velardi,et al.  Structural semantic interconnections: a knowledge-based approach to word sense disambiguation , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Adam Kilgarriff,et al.  The Senseval-3 English lexical sample task , 2004, SENSEVAL@ACL.

[7]  Jean Véronis,et al.  HyperLex: lexical cartography for information retrieval , 2004, Comput. Speech Lang..

[8]  Rada Mihalcea,et al.  Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling , 2005, HLT.

[9]  Eneko Agirre,et al.  Exploring feature spaces with svd and unlabeled data for Word Sense Disambiguation , 2005 .

[10]  Patrick Pantel,et al.  Discovering word senses from text , 2002, KDD.

[11]  Eneko Agirre,et al.  Two graph-based algorithms for state-of-the-art WSD , 2006, EMNLP.

[12]  Eneko Agirre,et al.  Evaluating and optimizing the parameters of an unsupervised graph-based WSD algorithm , 2006 .

[13]  Eneko Agirre,et al.  Unsupervised WSD based on Automatically Retrieved Examples: The Importance of Bias , 2004, EMNLP.

[14]  M. A. R T H A P A L,et al.  Making fine-grained and coarse-grained sense distinctions , both manually and automatically , 2005 .

[15]  Hinrich Schütze,et al.  Automatic Word Sense Discrimination , 1998, Comput. Linguistics.

[16]  Martha Palmer,et al.  The English all-words task , 2004, SENSEVAL@ACL.

[17]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[18]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[19]  George Karypis,et al.  Hierarchical Clustering Algorithms for Document Datasets , 2005, Data Mining and Knowledge Discovery.