TY - GEN
T1 - Inducing example-based semantic frames from a massive amount of verb uses
AU - Kawahara, Daisuke
AU - Peterson, Daniel W.
AU - Popescu, Octavian
AU - Palmer, Martha
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - We present an unsupervised method for inducing semantic frames from verb uses in giga-word corpora. Our semantic frames are verb-specific example-based frames that are distinguished according to their senses. We use the Chinese Restaurant Process to automatically induce these frames from a massive amount of verb instances. In our experiments, we acquire broad-coverage semantic frames from two giga-word corpora, the larger comprising 20 billion words. Our experimental results indicate the effectiveness of our approach.
AB - We present an unsupervised method for inducing semantic frames from verb uses in giga-word corpora. Our semantic frames are verb-specific example-based frames that are distinguished according to their senses. We use the Chinese Restaurant Process to automatically induce these frames from a massive amount of verb instances. In our experiments, we acquire broad-coverage semantic frames from two giga-word corpora, the larger comprising 20 billion words. Our experimental results indicate the effectiveness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=84905689474&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905689474&partnerID=8YFLogxK
U2 - 10.3115/v1/e14-1007
DO - 10.3115/v1/e14-1007
M3 - Conference contribution
AN - SCOPUS:84905689474
SN - 9781632663962
T3 - 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014
SP - 58
EP - 67
BT - 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014
PB - Association for Computational Linguistics (ACL)
T2 - 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014
Y2 - 26 April 2014 through 30 April 2014
ER -