137 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 7 DOI: 10.4018/978-1-60960-881-1.ch007 Hiram Calvo Nara Inst. of Science & Technology, Japan Kentaro Inui Tohoku University, Japan Yuji Matsumoto Nara Inst. of Science & Technology, Japan Learning Full-Sentence Co-Related Verb Argument Preferences from Web Corpora ABSTRACT Learning verb argument preferences has been approached as a verb and argument problem, or at most as a tri-nary relationship between subject, verb and object. However, the simultaneous correlation of all arguments in a sentence has not been explored thoroughly for sentence plausibility mensuration because of the increased number of potential combinations and data sparseness. In this work the authors pres- ent a review of some common methods for learning argument preferences beginning with the simplest case of considering binary co-relations, then they compare with tri-nary co-relations, and fnally they consider all arguments. For this latter, the authors use an ensemble model for machine learning using discriminative and generative models, using co-occurrence features, and semantic features in different arrangements. They seek to answer questions about the number of optimal topics required for PLSI and LDA models, as well as the number of co-occurrences that should be required for improving performance. They explore the implications of using different ways of projecting co-relations, i.e., into a word space, or directly into a co-occurrence features space. The authors conducted tests using a pseudo-disambiguation task learning from large corpora extracted from Internet.