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LINGUIST List 17.2013

Mon Jul 10 2006

Diss: Computational Ling: O'Hara: 'Empirical Acquisition of Concept...'

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        1.    Tom O'Hara, Empirical Acquisition of Conceptual Distinctions via Dictionary Definitions
Message 1: Empirical Acquisition of Conceptual Distinctions via Dictionary Definitions
Date: 10-Jul-2006
From: Tom O'Hara <tomoharaumbc.edu>
Subject: Empirical Acquisition of Conceptual Distinctions via Dictionary Definitions


Institution: New Mexico State University
Program: Computer Science
Dissertation Status: Completed
Degree Date: 2005

Author: Tom O'Hara

Dissertation Title: Empirical Acquisition of Conceptual Distinctions via Dictionary Definitions

Dissertation URL: http://www.cs.nmsu.edu/~tomohara/ohara-phd-thesis-nmsu05.pdf

Linguistic Field(s): Computational Linguistics

Dissertation Director:
Janyce Wiebe

Dissertation Abstract:

This thesis discusses the automatic acquisition of conceptual distinctions
using empirical methods, with an emphasis on semantic relations. The goal
is to improve semantic lexicons for computational linguistics, but the work
can be applied to general-purpose knowledge bases as well.

The approach is to analyze dictionary definitions to extract the
distinguishing information (i.e., differentia) for concepts relative to
their sibling concepts. A two-step process is employed to decouple the
definition parsing from the disambiguation of the syntactic relations into
the underlying semantic ones. Previous approaches tend to combine these
steps through pattern matching geared to particular types of relations. In
contrast, here a broad-coverage parser is first used to determine the
syntactic relationships, and then statistical classification techniques are
used to disambiguate the relationships into their underlying semantics.

There are several contributions of this thesis. First, it introduces an
empirical methodology for the extraction and disambiguation of semantic
relations from dictionary definitions. Second, it introduces a statistical
representation for these semantic relations using Bayesian networks, which
are popular in artificial intelligence for representing probabilistic
dependencies. Third, it shows how improvements in word-sense disambiguation
can be achieved by augmenting a standard statistical classifier approach
with a probabilistic spreading-activation system using the semantic
information extracted using this process.


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