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    Deriving verb predicates by clustering verbs with arguments.

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    Date Issued
    2017
    Author(s)
    Sedoc, João
    Wijaya, Derry
    Rouhizadeh, Masoud
    Schwartz, Andy
    Ungar, Lyle H.
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    Permanent Link
    https://hdl.handle.net/2144/40710
    Version
    First author draft
    Citation (published version)
    João Sedoc, Derry Wijaya, Masoud Rouhizadeh, Andy Schwartz, Lyle H Ungar. 2017. "Deriving Verb Predicates By Clustering Verbs with Arguments.." CoRR, Volume abs/1708.00416.
    Abstract
    Hand-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.
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    • CAS: Computer Science: Scholarly Papers [187]
    • BU Open Access Articles [3730]


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