Unsupervised grammar induction with Combinatory Categorial Grammars

Bisk, Yonatan Yitzhak

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https://hdl.handle.net/2142/89027 Copy

Description

Title

Unsupervised grammar induction with Combinatory Categorial Grammars

Author(s)

Bisk, Yonatan Yitzhak

Issue Date

2015-11-30

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

Hockenmaier, Julia

Doctoral Committee Chair(s)

Hockenmaier, Julia

Committee Member(s)

• Eisner, Jason
• Roth, Dan
• Zhai, ChengXiang

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Combinatory Categorial Grammar (CCG)
• Grammar Induction
• Unsupervised Methods

Abstract

Language is a highly structured medium for communication. An idea starts in the speaker's mind (semantics) and is transformed into a well formed, intelligible, sentence via the specific syntactic rules of a language. We aim to discover the fingerprints of this process in the choice and location of words used in the final utterance. What is unclear is how much of this latent process can be discovered from the linguistic signal alone and how much requires shared non-linguistic context, knowledge, or cues. Unsupervised grammar induction is the task of analyzing strings in a language to discover the latent syntactic structure of the language without access to labeled training data. Successes in unsupervised grammar induction shed light on the amount of syntactic structure that is discoverable from raw or part-of-speech tagged text. In this thesis, we present a state-of-the-art grammar induction system based on Combinatory Categorial Grammars. Our choice of syntactic formalism enables the first labeled evaluation of an unsupervised system. This allows us to perform an in-depth analysis of the system’s linguistic strengths and weaknesses. In order to completely eliminate reliance on any supervised systems, we also examine how performance is affected when we use induced word clusters instead of gold-standard POS tags. Finally, we perform a semantic evaluation of induced grammars, providing unique insights into future directions for unsupervised grammar induction systems.

Graduation Semester

2015-12

Type of Resource

text

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http://hdl.handle.net/2142/89027

Copyright and License Information

Copyright 2015 Yonatan Bisk

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