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dc.contributor.authorSedoc, Joãoen_US
dc.contributor.authorWijaya, Derryen_US
dc.contributor.authorRouhizadeh, Masouden_US
dc.contributor.authorSchwartz, Andyen_US
dc.contributor.authorUngar, Lyle H.en_US
dc.date.accessioned2020-05-08T15:40:34Z
dc.date.available2020-05-08T15:40:34Z
dc.date.issued2017
dc.identifier.citationJoão Sedoc, Derry Wijaya, Masoud Rouhizadeh, Andy Schwartz, Lyle H Ungar. 2017. "Deriving Verb Predicates By Clustering Verbs with Arguments.." CoRR, Volume abs/1708.00416.
dc.identifier.urihttps://hdl.handle.net/2144/40710
dc.description.abstractHand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage. Verb classes automatically induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other hand, can give clusters with much larger coverage, and can be adapted to specific corpora such as Twitter. We present a method for clustering the outputs of VerbKB: verbs with their multiple argument types, e.g.“marry(person, person)”, “feel(person, emotion).” We make use of a novel lowdimensional embedding of verbs and their arguments to produce high quality clusters in which the same verb can be in different clusters depending on its argument type. The resulting verb clusters do a better job than hand-built clusters of predicting sarcasm, sentiment, and locus of control in tweets.en_US
dc.language.isoen_US
dc.relation.ispartofCoRR
dc.titleDeriving verb predicates by clustering verbs with arguments.en_US
dc.typeArticleen_US
dc.description.versionFirst author draften_US
pubs.elements-sourcedblpen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
dc.identifier.mycv399380


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