Deep Natural Language Semantics - Raymond Mooney

Distinguished Lecture Series November 4, 2014 Raymond Mooney: "Deep Natural Language Semantics by Combining Logical and Distributional Methods using Probabilistic Logic" 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 for Semantic Textual Similarity (STS) we use Probabilistic Soft Logic (PSL). We present experimental results on standard benchmark datasets for these problems and emphasize the advantages of combining logical structure of sentences with statistical knowledge mined from large corpora.

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2014/11/26 Allen Institute for Artificial Intelligence (AI2)   Share on Facebook

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