Abstract
Language understanding and semantic inference are crucial for solving complex natural language applications, from intelligent personal assistants to automatic summarization systems. However, current systems often require hand-coded information about the domain of interest, an approach that will not scale up to the large array of possible domains and topics in text collections today. In this talk, I demonstrate the potential of distributional semantics (DS), an approach to modeling meaning by using the contexts in which a word or phrase appears, to assist in acquiring domain knowledge and to support the desired inference. I present a method that integrates phrasal DS representations into a probabilistic model in order to learn about the important events and slots in a domain, resulting in state-of-the-art performance on template induction and multi-document summarization for systems that do not rely on hand-coded domain knowledge. I also propose to evaluate DS by their ability to support inference, the hallmark of any semantic formalism. These results demonstrate the utility of DS for current natural language applications, and provide a principled framework for measuring progress towards automated inference in any domain
Biography
Jackie CK Cheung is a PhD candidate at the University of Toronto. His research interests span several areas of natural language processing, including computational semantics, automatic summarization, and natural language generation. His work is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), as well as a Facebook Fellowship.