TY - GEN
T1 - Co-related verb argument selectional preferences
AU - Calvo, Hiram
AU - Inui, Kentaro
AU - Matsumoto, Yuji
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2011.
PY - 2011
Y1 - 2011
N2 - Learning Selectional Preferences has been approached as a verb and argument problem, or at most as a tri-nary relationship between subject, verb and object. The correlation of all arguments in a sentence, however, has not been extensively studied for sentence plausibility measuring because of the increased number of potential combinations and data sparseness. We propose a unified model for machine learning using SVM (Support Vector Machines) with features based on topic-projected words from a PLSI (Probabilistic Latent Semantic Indexing) Model and PMI (Pointwise Mutual Information) as co-occurrence features, and WordNet top concept projected words as semantic classes. We perform tests using a pseudo-disambiguation task. We found that considering all arguments in a sentence improves the correct identification of plausible sentences with an increase of 10% in recall among other things.
AB - Learning Selectional Preferences has been approached as a verb and argument problem, or at most as a tri-nary relationship between subject, verb and object. The correlation of all arguments in a sentence, however, has not been extensively studied for sentence plausibility measuring because of the increased number of potential combinations and data sparseness. We propose a unified model for machine learning using SVM (Support Vector Machines) with features based on topic-projected words from a PLSI (Probabilistic Latent Semantic Indexing) Model and PMI (Pointwise Mutual Information) as co-occurrence features, and WordNet top concept projected words as semantic classes. We perform tests using a pseudo-disambiguation task. We found that considering all arguments in a sentence improves the correct identification of plausible sentences with an increase of 10% in recall among other things.
UR - http://www.scopus.com/inward/record.url?scp=79952254310&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-19400-9_11
DO - 10.1007/978-3-642-19400-9_11
M3 - Contribución a la conferencia
SN - 9783642193996
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 143
BT - Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings
T2 - 12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011
Y2 - 20 February 2011 through 26 February 2011
ER -