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Title:Improving a supervised CCG parser
Author(s):Musa, Ryan A
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Natural language processing
combinatory categorial grammar
Abstract:The central topic of this thesis is the task of syntactic parsing with Combinatory Categorial Grammar (CCG). We focus on pipeline approaches that have allowed researchers to develop efficient and accurate parsers trained on articles taken from the Wall Street Journal (WSJ). We present three approaches to improving the state-of-the-art in CCG parsing. First, we test novel supertagger-parser combinations to identify the parsing models and algorithms that benefit the most from recent gains in supertagger accuracy. Second, we attempt to lessen the future burdens of assembling a state-of-the-art CCG parsing pipeline by showing that a part-of-speech (POS) tagger is not required to achieve optimal performance. Finally, we discuss the deficiencies of current parsing algorithms and propose a solution that promises improvements in accuracy – particularly for difficult dependencies – while preserving efficiency and optimality guarantees.
Issue Date:2016-04-27
Rights Information:Copyright 2016 Ryan Musa
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05

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