From CLAYTONC"%ADONIS.MRGATE."A1.decnet@zeus.osti.gov Fri Dec 18 10:21:4
2 1992
···
Subject: Dissertations of Interest?
To: "gabriel" <gabriel@eid.anl.gov>
From: NAME: Charlynn Clayton
FUNC:
TEL: <CLAYTONC AT A1 AT ADONIS>
To: wins%"gabriel@eid.anl.gov"@zeus@mrgate
John:
I thought the two dissertation referenced in the attached
item might be of interest to you...in light of conversation we
have had previously.
Happy Holidays....Charlynn.
Author: IRLIST
Date: 16-Dec-1992
Posted-date: 18-Dec-1992
Subject: IR-L Digest, Vol.IX, No. 46, Issue 142
IRLIST Digest ISSN 1064-6965
December 16, 1992
Volume IX, Number 46
Issue 142
**********************************************************
IV. PROJECT WORK
C. Abstracts
1. IR-Related Dissertation Abstracts
**********************************************************
IV. PROJECT WORK
IV.C.1.
Fr: Susanne M. Humphrey <humphrey@nlm.nih.gov>
Re: Selected IR-Related Dissertation Abstracts
The following are citations selected by title and abstract as
being related to Information Retrieval (IR), resulting from a
computer search, using BRS Information Technologies, of the
Dissertation Abstracts Online database produced by University
Microfilms International (UMI). Included are UMI order number,
title, author, degree, year, institution; number of pages, one or
more Dissertation Abstracts International (DAI) subject
descriptors chosen by the author, and abstract. Unless otherwise
specified, paper or microform copies of dissertations may be
ordered from University Microfilms International, Dissertation
Copies, Post Office Box 1764, Ann Arbor, MI 48106; telephone for
U.S. (except Michigan, Hawaii, Alaska): 1-800-521-3042, for
Canada: 1-800-268-6090. Price lists and other ordering and
shipping information are in the introduction to the published
DAI. An alternate source for copies is sometimes provided.
Dissertation titles and abstracts contained here are published
with permission of University Microfilms International,
publishers of Dissertation Abstracts International (copyright by
University Microfilms International), and may not be reproduced
without their prior permission.
AN University Microfilms Order Number ADG92-17472.
AU BOOKMAN, LAWRENCE ALAN.
TI A TWO-TIER MODEL OF SEMANTIC MEMORY FOR TEXT COMPREHENSION.
IN Brandeis University Ph.D. 1992, 293 pages.
SO DAI V53(01), SecB, pp387.
DE Computer Science.
AB How can the background knowledge associated with our everyday
concepts be represented in a machine and how can we obtain and encode
such information. This thesis presents a new architecture for
semantic memory that provides a framework for addressing the
"background-knowledge" problem and discusses the implications of this
architecture for a model of text comprehension.
Semantic memory consists of two tiers: a relational tier that
represents the underlying structure of our cognitive world expressed
as a set of dependency relationships between concepts, and an analog
semantic feature (ASF) tier that represents the common or shared
knowledge about the concepts in the relational tier, expressed as a
set of statistical associations. I present an information theoretic
approach to automatically acquiring and encoding this knowledge from
on-line text corpora. In this approach the background knowledge
common to a community is encoded using a finite vocabulary of ASFs.
The ASFs used are based on the category structure of a thesaurus.
The two levels of semantic memory support two complementary views of
comprehension. One view, the "fine-grain" view, captures the many
details of interaction between context and world knowledge as
time-trajectories through concept space. This view permits a deeper
understanding of a text. A second view, the "coarse-grain" view,
captures in the form of a weighted semantic graph called an
interpretation graph, a set of explicit semantic relationships that
can be used to reason about the understanding of a text, which
includes the ability to summarize the text and extract what is
important. This view corresponds to a shallow understanding of the
text. Several computational techniques are presented for comparing
at two levels--the relational and the ASF--the underlying similarity
of two passages. The techniques developed are embodied in two
computer programs--LeMICON, a structured connectionist
implementation, and SSS, a symbolic implementation--designed to
explore the system's comprehension of 16 short texts from the stock
market domain.
The thesis describes an architecture and a mode of processing in
which memory is dynamic, exhibits hysteresis effects, and emphasizes
what is new about the effect of a given input on the knowledge
represented there.
AN University Microfilms Order Number ADG92-16026.
AU KNIGHT, KEVIN CRAWFORD.
TI INTEGRATING KNOWLEDGE ACQUISITION AND LANGUAGE ACQUISITION.
IN Carnegie-Mellon University Ph.D. 1991, 115 pages.
SO DAI V53(01), SecB, pp393.
DE Computer Science. Artificial Intelligence. Language, Linguistics.
AB Very large knowledge bases (KB's) constitute an important step for
artificial intelligence and will have significant effects on the
field of natural language processing. This thesis addresses the
problem of effectively acquiring two large bodies of formalized
knowledge: knowledge about the world (a KB), and knowledge about
words (a lexicon). The central observation is that these two bodies
of knowledge are highly redundant. For example, the syntactic
behavior of a noun (or a verb) is highly correlated with certain
physical properties of the object (or event) to which it refers. It
should be possible to take advantage of this type of redundancy in
order to greatly reduce both the time and expertise required to build
large KB's and lexicons.
This thesis describes LUKE, a software tool that allows a knowledge
base builder to create an English language interface by associating
words and phrases with KB entities. LUKE assumes no linguistic
expertise on the part of the user, because that expertise is built
directly into the tool itself. LUKE draws its power from a large set
of heuristics about how words are typically used to describe the
world. These heuristics exploit the redundancy between linguistic
and world knowledge. When a word or phrase is associated with some
KB entity, LUKE is able to accurately guess features of the word
based on features of the KB entity. LUKE can also hypothesize new
words and word senses based on the existence of others. All of
LUKE's hypotheses are displayed to the user for verification, using
a format designed to tap the user's basic linguistic intuitions.
LUKE stores its lexicon in the KB. Truth maintenance links ensure
that changes in the KB are automatically propagated to the lexicon.
LUKE compiles lexical entries into data structures convenient for
natural language parsing and generation programs. Lexicons acquired
by LUKE have been used by KBNL, a knowledge-based natural language
system, for applications in information retrieval, machine
translation, and KB navigation.
This work identifies several dozen heuristics that encode
redundancies between linguistic representations and representations
of world knowledge. It also demonstrates the usefulness of these
heuristics in a working lexical acquisition system.