MPhil Thesis Defence


"Knowledge-based Sense Pruning using the HowNet: an Alternative to Word
Sense Disambiguation"

By

Mr. Chi-Yung Wang


Abstract

In this thesis, we try to solve the problem of word sense disambiguation
(WSD) in natural language processing by sense pruning using a
knowledge-based approach.  Traditional WSD methods provide only one
meaning for each word in a passage. However, we believe that the textual
information alone may not be sufficient to determine the exact meaning of
each word which has to be resolved when higher-level knowledge becomes
available. Thus, we propose that the objective of WSD is to reduce the
number of plausible meanings of a word as much as possible through "sense
pruning". After sense pruning, we will associate a word with a list of
plausible meanings. We would like to keep the truly correct sense of each
word on its own meaning list and yet limit the number of possible meanings
of a whole sentence as small as possible.

We applied sense pruning to Chinese WSD, making use of the HowNet. HowNet
is a knowledge base that describes all entities in its database by a set
of unambiguous sememes. It provides information about the relationship
between concepts or their attributes, in which concepts are represented by
the sememes.  One of our contributions is integrating various knowledge
from HowNet for sense pruning, such as, relations between the sememes,
information structures in Chinese, relations of attributes and attribute
values, and characteristics of functional words.  Based on HowNet, four
additional databases were developed for sense pruning in this thesis.

We evaluated our sense pruning algorithm on the Corpus of Sinica from
Taiwan. Two criteria were used for the evaluation: recall rate and
reduction of the number of possible meanings of a sentence.  Effects of
the size of the analytical window and the analytical unit, and the speed
of the algorithm were fully studied.  In summary, sense pruning achieves a
recall rate of 91% while reducing the number of possible meanings of a
sentence by 48% when a whole sentence is taken as an analytical unit.


Date:			Monday, 14 January 2002

Time:			2:00p.m.-4:00p.m.

Venue:			Room 2302
			Lifts 17-18

Committee Members:	Dr. Brian Mak (Supervisor)
			Dr. Dr. Fangzhen Lin (Chairman)
			Dr. James Kwok
			Dr. Kok-Wee Gan


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