Difference between revisions of "RTE Knowledge Resources"

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Revision as of 04:21, 7 April 2009

RTE Knowledge Resources


Resource Type Author Brief description # Users
WordNet Lexical Princeton University Lexical database of English nouns, verbs, adjectives and adverbs 23 Users
Verbnet Lexical University of Colorado Boulder On-line lexicon for English verbs organized into classes 3 Users
VerbOcean Lexical University of Southern California Broad-coverage semantic network of verbs 5 Users
FrameNet Lexical Berkeley FrameNet project On-line lexical resource for English words, based on frame semantics (valences) and supported by corpus evidence 2 Users
NomBank Resources Lexical New York University Lexical Resources containing syntactic frames for nouns, extracted from annotated corpora 2 Users
Nomlex Plus Lexical Proteus Project - New York University Dictionary of English nominalizations: it describes the allowed complements for a nominalization and relates the nominal complements to the arguments of the corresponding verb 1 Users
Parc Polarity Lexicon Lexical PARC - Palo Alto Research Center Verbs classification with respect to semantic polarity 1 Users
DIRT Paraphrase Collection Collections of paraphrases (DIRT output) Various Output of the DIRT algorithm 4 Users
PropBank Resources Lexical University of Colorado Boulder Lexical Resources containing syntactic frames for verbs, extracted from annotated corpora 2 Users
TEASE Collection Syntactic-semantic Bar Ilan University Collection of Entailment Rules 0 Users
BADC Acronym and Abbreviation List Word List BADC - British Atmospheric Data Centre Acronym and Abbreviation List 1 Users
Acronym Guide Word List Acronym-Guide.com Acronym and Abbreviation Lists for English, branched in thematic directories 1 Users
Dekang Lin’s Thesaurus Thesaurus Dekang Lin - University of Alberta Thesaurus automatically constructed using a parsed corpus, based on distributional similarity scores 1 Users
Web1T 5-grams Word list Google Inc. Data set containing English word n-grams and their observed frequency counts. The n-gram counts were generated from approximately 1 trillion word tokens of text from publicly accessible Web pages 1 Users
Users
Users
Users
Users
Users
Users
Users