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Please use this identifier to cite or link to this item: http://hdl.handle.net/10012/6591

Title: A Semi-Supervised Approach to the Construction of Semantic Lexicons
Authors: Ahmadi, Mohamad Hasan
Keywords: bootstrapping
semantic lexicon induction
Approved Date: 21-Mar-2012
Date Submitted: 14-Mar-2012
Abstract: A growing number of applications require dictionaries of words belonging to semantic classes present in specialized domains. Manually constructed knowledge bases often do not provide sufficient coverage of specialized vocabulary and require substantial effort to build and keep up-to-date. In this thesis, we propose a semi-supervised approach to the construction of domain-specific semantic lexicons based on the distributional similarity hypothesis. Our method starts with a small set of seed words representing the target class and an unannotated text corpus. It locates instances of seed words in the text and generates lexical patterns from their contexts; these patterns in turn extract more words/phrases that belong to the semantic category in an iterative manner. This bootstrapping process can be continued until the output lexicon reaches the desired size. We explore employing techniques such as learning lexicons for multiple semantic classes at the same time and using feedback from competing lexicons to increase the learning precision. Evaluated for extraction of dish names and subjective adjectives from a corpus of restaurant reviews, our approach demonstrates great flexibility in learning various word classes, and also performance improvements over state of the art bootstrapping and distributional similarity techniques for the extraction of semantically similar words. Its shallow lexical patterns also prove to perform superior to syntactic patterns in capturing the semantic class of words.
Program: Computer Science
Department: School of Computer Science
Degree: Master of Mathematics
URI: http://hdl.handle.net/10012/6591
Appears in Collections:Electronic Theses and Dissertations (UW)
Faculty of Mathematics Theses and Dissertations

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