17.2013, Diss: Computational Ling: O'Hara: 'Empirical Acquisition of Concept...'

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LINGUIST List: Vol-17-2013. Mon Jul 10 2006. ISSN: 1068 - 4875.

Subject: 17.2013, Diss: Computational Ling: O'Hara: 'Empirical Acquisition of Concept...'

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1)
Date: 10-Jul-2006
From: Tom O'Hara < tomohara at umbc.edu >
Subject: Empirical Acquisition of Conceptual Distinctions via Dictionary Definitions 

	
-------------------------Message 1 ---------------------------------- 
Date: Mon, 10 Jul 2006 14:48:17
From: Tom O'Hara < tomohara at umbc.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(s):
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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