Peter Hayward (PhD-I).

Topic-based unsupervised semantic parsing

Users are overwhelmed by the volume of information they have access to. Thus being able to reliably and accurately search information becomes crucial to for work and in daily life. Today search terms used by search engines are typically based on keywords. Phrases and sentences are in use in some systems, but are rarely an option for the average user.

Semantic parsers can be used to develop better knowledge extraction systems, since they translate natural language sentences provided by the user to a formal ma- chine representation. However, the current state-of-the-art semantic parsers cannot interpret context based on the topics contained in the sentence and the document it originates from.

We therefore propose to develop a topic-based unsupervised semantic parsing framework that leverages observed and inferred topical information from corpora to disambiguate the semantic content of individual sentences. This leads to a parser that can be used on domain-independent corpora, unlike its predecessors.

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