Deriving verb predicates by clustering verbs with arguments.
Files
First author draft
Date
2017
DOI
Authors
Sedoc, João
Wijaya, Derry
Rouhizadeh, Masoud
Schwartz, Andy
Ungar, Lyle H.
Version
First author draft
OA Version
Citation
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.