Lin, Dekang - Department of Computing Science, University of Alberta

• An InformationTheoretic Definition of Similarity Department of Computer Science
• DIRT --Discovery of Inference Rules from Text Dekang Lin and Patrick Pantel
• Unsupervised Learning of Disambiguation Rules for Part of Speech Tagging
• Discovery of Inference Rules for Question Answering DEKANG LIN AND PATR ICK PANTEL
• Efficiently Clustering Documents with Committees Patrick Pantel and Dekang Lin
• Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity
• Discovering Word Senses from Text Patrick Pantel and Dekang Lin
• Word Alignment with Cohesion Constraint Dekang Lin and Colin Cherry
• Dependencybased Evaluation of Department of Computer Science
• ContextFree Grammar Parsing by Message Passing Dekang Lin Randy Goebel
• A Statistical CorpusBased Term Extractor 1 A Statistical CorpusBased Term Extractor
• An Information-Theoretic Definition of Similarity Department of Computer Science
• A Probabilistic Theory of Abductive Diagnostic Dekang Lin and Randy Goebel
• Word Alignment with Cohesion Constraint Dekang Lin and Colin Cherry
• A Probabilistic Network of Predicates Department of Computer Science
• University of Manitoba: Description of the PIE System Used for MUC6
• Discovering Word Senses from Text Patrick Pantel and Dekang Lin
• Word-for-Word Glossing with Contextually Similar Words Patrick Pantel and Dekang Lin
• Automatic Retrieval and Clustering of Similar Words Department of Computer Science
• Programming Quick Reference Guide
• CONFERENCE PROGRAM Sunday, July 25, 2004
• Submitted to Information Retrieval --INRT 34-99 October 4, 1999 1999 National Research Council Canada
• Chart Parsing Doug Arnold
• An Unsupervised Approach to Prepositional Phrase Attachment using Contextually Similar Words
• WordforWord Glossing with Contextually Similar Words Patrick Pantel and Dekang Lin
• University of Manitoba: Description of the NUBA System as Used for MUC5
• Program Recognition by Observation Department of Computer Science
• Extracting Collocations from Text Corpora Department of Computer Science
• Concept Discovery from Text Dekang Lin and Patrick Pantel
• Automatic Identi cation of Non-compositional Phrases Department of Computer Science
• A Probability Model to Improve Word Alignment Colin Cherry and Dekang Lin
• Identifying Synonyms among Distributionally Similar Words Dekang Lin and Shaojun Zhao
• In Proceedings of ACL93 PRINCIPLEBASED PARSING WITHOUT OVERGENERATION 1
• Identifying Synonyms among Distributionally Similar Words Dekang Lin and Shaojun Zhao
• DIRT Discovery of Inference Rules from Text Dekang Lin and Patrick Pantel
• Automatic Retrieval and Clustering of Similar Words Department of Computer Science
• Efficient Parsing for Korean and English: A Parameterized Message
• To appear in Proceedings of IJCAI95 A Dependencybased Method for Evaluating BroadCoverage Parsers
• Induction of Semantic Classes from Natural Language Text
• Regular Expression HOWTO Release 0.03
• The Probability of Causal Explanation Dekang Lin and Randy Goebel
• PRINCIPAR---An Efficient, Broadcoverage, Principlebased Parser
• Efficiently Clustering Documents with Committees Patrick Pantel and Dekang Lin
• Document Clustering with Committees Patrick Pantel and Dekang Lin
• UNIVERSITY OF ALBERTA RELEASE FORM
• Induction of Semantic Classes from Natural Language Text
• Document Clustering with Committees Patrick Pantel and Dekang Lin
• A Statistical Corpus-Based Term Extractor 1 A Statistical Corpus-Based Term Extractor
• Discovery of Inference Rules for Question Answering DEKANG LIN AND PATRICK PANTEL