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Published on Jul 22, 2016
Representing Meaning with a Combination of Logical and Distributional Models
As the field of Natural Language Processing develops, more ambitious semantic tasks are being addressed, such as Question Answering (QA) and Recognizing Textual Entailment (RTE). Solving these tasks requires (ideally) an in-depth representation of sentence structure as well as expressive and flexible representations at the word level. We have been exploring a combination of logical form with distributional as well as resource-based information at the word level, using Markov Logic Networks (MLNs) to perform probabilistic inference over the resulting representations. In this talk, I will focus on the three main components of a system we have developed for the task of Textual Entailment: (1) Logical representation for processing in MLNs, (2) lexical entailment rule construction by integrating distributional information with existing resources, and (3) probabilistic inference, the problem of solving the resulting MLN inference problems efficiently. I will also comment on how I think the ideas from this system can be adapted to Question Answering and the more general task of in-depth single-document understanding.