University of Illinois Urbana-Champaign

Improving a supervised CCG parser

Musa, Ryan A

Content Files
MUSA-THESIS-2016.pdf

Permalink

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

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
Date of Ingest
2016-07-07T19:58:16Z
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

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois