Newsgroups: bit.listserv.csg-l
Date:         Fri, 18 Dec 1992 12:52:51 CST
From:         John Gabriel <gabriel@EID.ANL.GOV>
Subject:      FYI - perhaps of interest

        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.