Abstract
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.
This research is supported by SNI, SIP-IPN, COFAA-IPN, and PIFI-IPN, CONACYT; and the Japanese Government (JSPS).
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Calvo, H., Inui, K., Matsumoto, Y. (2011). Co-related Verb Argument Selectional Preferences. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_11
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