TY - GEN
T1 - Single classifier approach for verb sense disambiguation based on generalized features
AU - Kawahara, Daisuke
AU - Palmer, Martha
N1 - Funding Information:
This work was supported by Kyoto University John Mung Program and JSPS KAKENHI Grant Number 25540140. We also gratefully acknowledge the support of the National Science Foundation Grant NSF-IIS-1116782, A Bayesian Approach to Dynamic Lexical Resources for Flexible Language Processing. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We also appreciate the fruitful discussions with Dr. Jinying Chen
Funding Information:
This work was supported by Kyoto University John Mung Program and JSPS KAKENHI Grant Number 25540140. We also gratefully acknowledge the support of the National Science Foundation Grant NSF-IIS-1116782, A Bayesian Approach to Dynamic Lexical Resources for Flexible Language Processing. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We also appreciate the fruitful discussions with Dr. Jinying Chen.
PY - 2014
Y1 - 2014
N2 - We present a supervised method for verb sense disambiguation based on VerbNet. Most previous supervised approaches to verb sense disambiguation create a classifier for each verb that reaches a frequency threshold. These methods, however, have a significant practical problem that they cannot be applied to rare or unseen verbs. In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text. This single classifier also exploits generalized semantic features of a verb and its modifiers in order to better deal with rare or unseen verbs. Our experimental results show that the proposed method achieves equivalent performance to per-verb classifiers, which cannot be applied to unseen verbs. Our classifier could be utilized to improve the classifications in lexical resources of verbs, such as VerbNet, in a semi-automatic manner and to possibly extend the coverage of these resources to new verbs.
AB - We present a supervised method for verb sense disambiguation based on VerbNet. Most previous supervised approaches to verb sense disambiguation create a classifier for each verb that reaches a frequency threshold. These methods, however, have a significant practical problem that they cannot be applied to rare or unseen verbs. In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text. This single classifier also exploits generalized semantic features of a verb and its modifiers in order to better deal with rare or unseen verbs. Our experimental results show that the proposed method achieves equivalent performance to per-verb classifiers, which cannot be applied to unseen verbs. Our classifier could be utilized to improve the classifications in lexical resources of verbs, such as VerbNet, in a semi-automatic manner and to possibly extend the coverage of these resources to new verbs.
KW - Single classifier
KW - Verb sense disambiguation
KW - Word representations
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M3 - Conference contribution
AN - SCOPUS:85030222615
T3 - Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014
SP - 4210
EP - 4213
BT - Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014
A2 - Calzolari, Nicoletta
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Declerck, Thierry
A2 - Mariani, Joseph
A2 - Maegaard, Bente
A2 - Moreno, Asuncion
A2 - Odijk, Jan
A2 - Mazo, Helene
A2 - Piperidis, Stelios
A2 - Loftsson, Hrafn
PB - European Language Resources Association (ELRA)
T2 - 9th International Conference on Language Resources and Evaluation, LREC 2014
Y2 - 26 May 2014 through 31 May 2014
ER -