Traditional logical approaches to semantics and newer distributional or vector space approaches have complementary strengths and weaknesses.We have developed methods that integrate logical and distributional models by using a CCG-based parser to produce a detailed logical form for each sentence, and combining the result with soft inference rules derived from distributional semantics that connect the meanings of their component words and phrases. For recognizing textual entailment (RTE) we use Markov Logic Networks (MLNs) to combine these representations and we present results on standard corpora emphasizing the advantages of combining logical structure of sentences with statistical knowledge mined from large corpora.
Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, but currently on leave at Microsoft Research. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 150 published research papers, primarily in the areas of machine learning and natural language processing. He was the President of the International Machine Learning Society from 2008-2011, program co-chair for AAAI 2006, general chair for HLT-EMNLP 2005, and co-chair for ICML 1990. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery, and the recipient of best paper awards from AAAI-96, KDD-04, ICML-05 and ACL-07.