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IRList Digest Volume 2 Number 23
IRList Digest Friday, 9 May 1986 Volume 2 : Issue 23
Today's Topics:
Query - Interest in Vector Processors and Document Similarities
Announcement - Job in AI and Education
COGSCI - Probability, Maximum Entropy, and Expert Systems
CSLI - Models in Semantics; Prolog and Geometry
CSLI - Structures in Written Language; Functional-Typological Syntax;
On Visual Communication
CSLI - German-English Transfer of f-structures
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Date: Mon, 5 May 86 13:38:15 edt
From: ltw@eecs.umich.CSNET
Subject: Ed, FYI
Date: Mon, 28 Apr 86 16:30:44+0100
From: user <seismo!mcvax!unizh!gross%nyu.ARPA@zippy.eecs>
Subject: (forwarded) request for information
Resent-From: Lou Salkind <salkind%nyu.ARPA@zippy.eecs>
Resent-To: supercomputer@nyu.ARPA
Resent-Date: Wed, 30 Apr 86 11:45:10 EDT
I am working on a non-numerical problem (document similarity
computations in information retrieval) with a VP200 vector processor.
Do you know of other people working on non-numerical problems with
vector processors. I would like to get in contact with other
researchers in this field and I thought you might have some addresses.
Thank you in advance
Juerg Grossmann
c/o Institute of Informatics
University of Zurich
Winterthurerstrasse 190
CH-8057 Zurich
...!cernvax!unizh!circe!gross
------------------------------
Date: Thu, 8 May 86 00:53:50 edt
From: RICHER@SUMEX-AIM.ARPA
To: richer@sumex-aim.arpa
Subject: JOB ANNOUNCEMENT
SIMON FRASER UNIVERSITY
A major new initiative has resulted in the following
positions for which applications are invited.
o Knowledge Engineer
o Post-doctoral Fellow
o Research Scientist
The Instructional Psychology Research Group is seeking
qualified researchers trained in AI techniques. Applicants
will join our staff in work aimed at transfering basic
AI technology to educational applications and conducting
specific research in:
o Expert Systems
o Planning Systems
o Knowledge Understanding and Representation
o Intelligent CAI
The successful applicant will have an advanced degree in
computer science and demonstrated experience in at least
one of the aforementioned areas. Experience with Symbolics
hardware, LISP, PROLOG and other AI languages is desirable.
Preference will be given to applicants eligible for
employment in Canada at the time of application.
For further information contact
Dr. Philip H. Winne
Director, Instructional Psychology Research Group
Faculty of Education
Simon Fraser University
Burnaby, British Columbia V5A 1S6
(604) 291-3395
Questions may be directed to the above address, or:
Wolfgang_Rothen%SFU.Mailnet@MIT-Multics.ARPA
------------------------------
Date: Mon, 5 May 86 13:37:53 edt
From: DEJONG%OZ.AI.MIT.EDU@mc.lcs.mit.edu
Subject: Cognitive Science Calendar [Extract - Ed]
From- Lisa F. Melcher <LISA at XX.LCS.MIT.EDU>
Monday, 5 May 4:00pm Room: NE43-453
AUTOMATIC INDUCTION OF PROBABILISTIC EXPERT SYSTEMS
Peter Cheeseman
NASA Ames
Many have realized that expert systems that make decisions under uncertainty
must represent this uncertainty and manipulate it correctly. This cannot be
done in general by "symbolic" (i.e. non-numeric) methods or by sprinkling
numbers over logical inference, as advocated by many authors in AI.
Probability has been proved to be the only consistent inference scheme if
uncertainty is represented by a real number. Probabilistic inference
requires assessing the effect of ALL the relevent evidence on the hypothesis
of interest though ALL the possible chains of inference (rather than
establishing a single path from axioms to theorem, as in logic). However,
some methods used in probabilistic inference in AI (e.g. Prospector) impose
strong constraints on the structure of the information (e.g. conditional
independence) or require large amounts of information. The solution to this
problem is to use Maximum Entropy to spread the uncertainty over the set of
possibilities as evenly as possible consistent with the known information. A
computationally efficient method for performing the maximum entropy
calculation will be presented as well as a method for extracting the
necessary probabilistic information directly from data. The result is a
complete probabilistic expert system without using an expert.
Sponsored by TOC, Laboratory for Computer Science
Ronald Rivest, Host
------------------------------
Date: Thu, 24 Apr 86 01:34:20 est
From: EMMA@su-csli.ARPA
Subject: Calendar, April 24, No. 13 [Extracted and edited - Ed]
THIS WEEK'S SEMINAR (April 24, 1986)
Uses and Abuses of Models in Semantics
Jon Barwise and John Etchemendy
Barwise@su-csli and Etchemendy@su-csli
The use of set-theoretic models as a way to study the semantics of
both natural and computer languages is a powerful and important
technique. However, it is also fraught with pitfalls for those who do
not understand the nature of modeling. In this talk we hope to show
how a proper understanding of the representation relationship implicit
in modeling can help one exploit the power while avoiding the
pitfalls. ... The talk will presuppose some familiarity with the
techniques under discussion.
PIXELS AND PREDICATES
Prolog and Geometry
Randolph Franklin, UC at Berkeley
wrf@degas.berkeley.edu
1:00 p.m., Tuesday, April 29, CSLI trailers
The Prolog language is a useful tool for geometric and graphics
implementations because its primitives, such as unification, match the
requirements of many geometric algorithms. We have implemented
several problems in Prolog including a subset of the Graphics Kernal
Standard, convex hull finding, planar graph traversal, recognizing
groupings of objects, and boolean combinations of polygons using
multiple precision rational numbers. Certain paradigms, or standard
forms, of geometric programming in Prolog are becoming evident. They
include applying a function to every element of a set, executing a
procedure so long as a certain geometric pattern exists, and using
unification to propagate a transitive function. Certain strengths and
weaknesses of Prolog for these applications are now apparent.
------------------------------
Date: Thu, 1 May 86 18:41:21 edt
From: EMMA@su-csli.ARPA
Subject: Calendar, May 1, No. 1 [Extracted and edited - Ed]
THIS WEEK'S COLLOQUIUM
Structures in Written Language
Geoff Nunberg (Nunberg@csli)
4:15, Thursday, May 1, Redwood G-19
Just about all contemporary research on linguistic structure has
been based exclusively on observations about the spoken language; the
written language, when it is talked about at all, is generally taken
to be derivative of speech, and without any independent theoretical
interest. When we consider the written language in its own terms,
however, it turns out to have a number of distinctive features and
structures. In particular, it contains a number of explicitly
delimited ``text categories,'' such as are indicated by the common
punctuation marks and related graphical features, which are either
wholly absent in the spoken language, or at best are present there
only implicitly. In the course of uncovering the principles that
underlie the use of text categories like the text-sentence, paragraph,
and parenthetical (i.e., a string delimited by parentheses), we have
to provide three levels of grammatical description: a semantics, which
sets out the rules of interpretation associated with text categories
by associating each type with a certain type of informational unit; a
syntax, which sets out the dependencies that hold among category-types;
and a graphology, which gives the rules that determine how instances
of text categories will be graphically presented. Each of these
components is a good deal more complex and less obvious than one might
suppose on the basis of a recollection of what the didactic grammars
have to say about the written language; what emerges, in fact, is that
most of the rules that determine how text delimiters are used are not
learned through explicit instruction, and are no more accessible to
casual reflection than are the rules of grammar of the spoken
language.
NEXT WEEK'S TINLUNCH (5/8/86)
Definiteness and Referentiality
Vol. 1, Ch. 11 of
Syntax: A Functional-Typological Introduction
by Talmy Givon
Discussion led by Mark Johnson (Johnson@csli)
The relationship between syntactic structure and meaning is one of
the most interesting lines of research being undertaken here at CSLI.
One of the questions being addressed in this work concerns the way
that grammatical or syntactic properties of an utterance interact with
its semantics, i.e., what it means. Givon and others claim that
discourse notions of topicality and definiteness interact strongly
with grammatical processes such as agreement---and moreover, that
there is no clear dividing line between grammar and discourse; one
cannot understand agreement or anaphora viewing them as purely
grammatical processes. Linguists here at CSLI are tentatively moving
toward this position, for example Bresnan and Mchombo (1986) make
explicit use of a theory of ``discourse functions'' to explain the
distributional properties of Object Marking in Chichewa, so a
discussion of what it would mean to have an ``integrated'' theory of
language is quite timely.
Givon's treatment of definiteness and referentiality explicitly
rejects earlier philosphical treatments as being ``too restrictive to
render a full account of the facts of human language.'' He starts by
listing some observations on the interactions between definiteness and
a variety of other linguistic phenomena (e.g. modality) and goes on to
propose a model based on a ``Universe of Discourse'' and the notion of
``referential intent.'' After examining examples of how
referentiality is coded in various languages and how it interacts with
various other syntactic and semantic phenomena, he finishes by
discussing degrees of definiteness and referentially, and introduces
the notion of communicative importance.
This chapter raised several interesting questions. For example,
what are the key properties of referentiality and definiteness, and
how would one go about building a theory that expresses them? What
are Givon's insights into this matter, and how could these be
reconstructed within a formal theory such as DRS theory or Situation
Semantics?
NEXT WEEK'S SEMINAR (5/8/86)
On Visual Communication
David Levy, Xerox Palo Alto Research Center (Dlevy.pa@xerox)
Lately there has been much talk around CSLI about representation as
a concept transcending and unifying work being done in different
research groups and domains. Various points have emerged and recurred
in recent presentations and discussions: the distinction between the
representing state of affairs (A) and the state of affairs represented
(B); examples of the dangers inherent in conflating them; forms of
structural correspondence between aspects (objects, properties, and
relations) of A and aspects of B; the partiality of representation
(the fact that only certain aspects of A correspond to aspects of B,
and that only certain aspects of B correspond to aspects of A); the
priority of B over A; and so on.
The use of computers is largely mediated by representations. Many
of these are transparent to us: We talk of ``typing an A'' when we
actually press a key, causing a character code (a character
representation) to be generated from which an actual character is
rendered. We talk of ``viewing'' data structures, when in fact we do
nothing of the sort, since data structures ``inside'' machines are
inherently non-visual, much as are mental states ``inside'' heads;
rather, we view *visual representations* of data structures.
In many contexts the transparency of representations (leading to
the conflation of A and B) is tremendously useful and powerful. The
term ``direct manipulation'' denotes a style of user interface design in
which the user is led (or encouraged) to conflate the visual objects
on the screen (e.g. icons) with the things they represent (e.g.
printers), and to conflate the representation of these visual objects
with the visual objects themselves. But there seem to be times when
our facility for seeing through representations is a hindrance rather
than a help, as Barwise and Etchemendy argued recently for the case of
model theory.
As a theoretician and observer of certain classes of computer
systems, and, equally importantly, as a *designer* of them, I believe
that we need an understanding of representation (and of the sorts of
issues described in the first paragraph) to help us build truly
rational systems. In this talk I will focus on the problem of
developing an analysis of visual representation. I will use examples
from the surface of computer screens (e.g. windows, scroll bars, and
icons) to illustrate the importance of distinctions such as visual vs.
non-visual entities, representing vs. represented entities, and
(active) processes vs. (static) representation relations.
------------------------------
Date: Thu, 8 May 86 00:53:56 edt
From: EMMA@SU-CSLI.ARPA
Subject: Calendar, May 8, No. 15 [Extracted - Ed]
NEXT WEEK'S COLLOQUIUM (May 15, 1986)
Transfer of f-structures Across Natural Languages
Tom Reutter, Weidner Communications Corp., Chicago
A recursive algorithm for mapping functional structure from a
source natural language into a target natural language is presented
and its implementation in the programming language CPROLOG is
discussed. The transfer algorithm is guided by a symmetrical
bilingual lexicon. It was prototypically implemented for
German-English as part of a transfer-oriented machine translation
system at the University of Stuttgart (Germany). Special emphasis is
placed on asymmetiral transfer, e.g., mapping of f-structures with
different semantic valencies, unequal NUM and SPEC attributes, etc.
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END OF IRList Digest
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