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Unsupervised methods for developing taxonomies by combining syntactic and statistical information
Dominic Widdows
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Paper Details:
Year: 2003
Venue:
HLT |
NAACL |
Citations
URL
A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web
Zornitsa Kozareva
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Eduard Hovy
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Discovering Corpus-Specific Word Senses
Beate Dorow
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Dominic Widdows
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Dependency-Based Construction of Semantic Space Models
Sebastian Padó
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Mirella Lapata
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Semantic Vectors: a Scalable Open Source Package and Online Technology Management Application
Dominic Widdows
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Kathleen Ferraro
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Unknown word sense detection as outlier detection
Katrin Erk
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Towards a Matrix-based Distributional Model of Meaning
Eugenie Giesbrecht
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Orthogonal Negation in Vector Spaces for Modelling Word-Meanings and Document Retrieval
Dominic Widdows
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Learning Word Sense With Feature Selection and Order Identification Capabilities
Zheng-Yu Niu
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Dong-Hong Ji
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Chew-Lim Tan
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Supersense Tagging of Unknown Nouns Using Semantic Similarity
James Curran
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Structured Learning for Taxonomy Induction with Belief Propagation
Mohit Bansal
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David Burkett
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Gerard de Melo
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Dan Klein
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Selecting Corpus-Semantic Models for Neurolinguistic Decoding
Brian Murphy
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Partha Talukdar
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Tom Mitchell
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An Empirical Model of Multiword Expression Decomposability
Timothy Baldwin
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Colin Bannard
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Takaaki Tanaka
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Dominic Widdows
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Improving Context Vector Models by Feature Clustering for Automatic Thesaurus Construction
Jia-Ming You
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Keh-Jiann Chen
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Semantic Density Analysis: Comparing Word Meaning across Time and Phonetic Space
Eyal Sagi
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Stefan Kaufmann
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Brady Clark
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Exploration of functional semantics of prepositions from corpora of descriptions of visual scenes
Simon Dobnik
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John Kelleher
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http://infomap.stanford.edu/classes
http://infomap.stanford.edu/webdemo
Field Of Study
Task
Information Retrieval
Mathematical Models
Language
English
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