Improving a supervised CCG parser

Musa, Ryan A

Permalink

https://hdl.handle.net/2142/90677 Copy

Description

Title

Improving a supervised CCG parser

Author(s)

Musa, Ryan A

Issue Date

2016-04-27

Director of Research (if dissertation) or Advisor (if thesis)

Hockenmaier, Julia

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Natural language processing
• combinatory categorial grammar
• parsing

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.

Graduation Semester

2016-05

Type of Resource

text

Permalink

http://hdl.handle.net/2142/90677

Copyright and License Information

Copyright 2016 Ryan Musa

Owning Collections