nl-kr-request@CS.ROCHESTER.EDU (NL-KR Moderator Brad Miller) (12/01/88)
NL-KR Digest (11/30/88 20:37:01) Volume 5 Number 30
Today's Topics:
query on matching of knowledge representation structures
HPSG??
Machine Learning, K.R. and CogSci Grad programs?
sense of wholeness of one's surroundings
matched of KR structures
Inherit through net
Re: Inherit through net
Visiting position in Natural Language Understanding
UCSD PhD. program in Cognitive Science
Assistant Professor position at MIT
NSF Program in Knowledge Models and Cognitive Systems
Submissions: NL-KR@CS.ROCHESTER.EDU
Requests, policy: NL-KR-REQUEST@CS.ROCHESTER.EDU
----------------------------------------------------------------------
Date: Thu, 3 Nov 88 15:49 EST
From: LEWIS@cs.umass.EDU
Subject: query on matching of knowledge representation structures
Can anyone point me to some references on matching of subparts of
frame-based knowledge representation structures? Essentially what I'm
interested in is equivalent to finding some/all/the biggest of the
isomorphic subgraphs of two directed graphs, except that edges and vertices
are labeled, and there are restrictions on what labels are allowed to match.
For additional fun, there might be weights on the edges and vertices as
well, and you might not just be interested in large-sized isomorphic
subgraphs, but in maximal scoring ones.
Still more interesting would be if anything has been done on the case where
you can inferences to the structures before matching, so that you actually
have to search a space of alternative representations, as well as comparing
them.
Suggestions? If text content matching had been a bigger application of NLP
in the past, there'd be a bunch of stuff on this, but as it is, I suspect that
vision or case based reasoning people may have done more on this.
Best,
David D. Lewis ph. 413-545-0728
Computer and Information Science (COINS) Dept. BITNET: lewis@umass
University of Massachusetts, Amherst ARPA/MIL/CS/INTERnet:
Amherst, MA 01003 lewis@cs.umass.edu
USA
UUCP: ...!uunet!cs.umass.edu!lewis@uunet.uu.net
------------------------------
Date: Tue, 8 Nov 88 14:52 EST
From: James.Price.Salsman@cat.cmu.edu
Subject: HPSG??
Folks,
I've been folowing the discussion intently. I know what
GPSG is, but I have never come across HPSG -- could someone
give me a introductory and a difinative reverence?
Also, how are all of you production-based linguists doing
with the popularity surge in connectionist computation?
How do you account for the large amount of ungrammatical
conversation that takes place?
--
:James P. Salsman (jps@CAT.CMU.EDU)
------------------------------
Date: Tue, 8 Nov 88 15:57 EST
From: hadj@sbcs.sunysb.edu
Subject: Machine Learning, K.R. and CogSci Grad programs?
I am looking for computer science graduate programs which
are strong in the areas of Machine Learning and Knowledge
Representation. Also, programs in Cognitive Science are of
particular interest.
Please e-mail any suggestions, and I can post a summary.
Thanks in advance,
-mike hadjimichael. hadj@sbcs.sunysb.edu
{philabs, allegra}!sbcs!hadj hadj%sbcs.sunysb.edu@sbccvm.bitnet
< departmentofcomputersciencesunystonybrookstonybrooknyoneonesevenninefour >
------------------------------
Date: Fri, 18 Nov 88 10:57 EST
From: Roland Zito-wolf <rjz@cs.brandeis.edu>
Subject: sense of wholeness of one's surroundings
I am looking for references to work dealing with the way that the
human mind manages to maintain the impression of being aware of one's
surroundings as a whole, in spite of the fact that the visual system
(and other senses) can only attend to a small portion (aspect) of it
at any one time. The mind is in some sense "integrating" lots of
information together that was gathered over time, without conscious
effort, such that we feel we percieve it all simultaneously.
If this seems obvious, consider how the world looks through a
limited aperture like a cardboard tube, such that you can really only
see a small portion of the environment at any time, and have to
exsplicitly aim yourself at whatever you want to see.
A related question is: How is it that we maintain the impression of
perceiving (or recalling, or imagining) our surroundings in great
detail even when we've given them but the slightest glance-- as when
we enter a room with which we are familiar. We know the texture and
color of the walls, the contents of the room, and so forth, at some
half-conscious level, without actually paying them much attention. We
function quite adequately using only these "general" impressions. Yet
if we have a reason to care about such items, we integrate that data
into our surroundings-model seamlessly, as if it were there all along.
For example, if one actually looks at the details of a textured
surface, suddenly each little feature can be perceived. When we look
away, the details are quickly lost, but the high-level impression
remains.
I am interested in discussions of these issues in terms that can be
related to questions of knowledge-representation, the frame problem,
and such.
Thanks,
rjz.
------------------------------
Date: Wed, 23 Nov 88 12:27 EST
From: Roland Zito-wolf <rjz@cs.brandeis.edu>
Subject: matched of KR structures
Last year i sent out a similar query and recieved a number of
useful and interesting responses. (as that was already posted, i havent
repeated it here.) THis problem has interested me for a while
(as a subproblem of KR). I think its really a larger or wider
problem than it first appears.
All sorts of things are relevant to this problem, depending on how one
formulates it. For example, one might take inspiration from the fact that
strings are just a special type of directed graph, and look at algorithms
for determining the "difference" between two strings, aka the minimum
number of operations required to xform one to the other:
Wagner and Fischer, The String-to-string correction problem JACM Jan 1974.
Lowance and Wagner, An extension to ... , JACM April 1975
various references to quick string-search algorithms, such as Boyer-Moore
Hall & Dowling, Approximate String Matching, Computing Surveys, Dec 80
If one allows one to have alternative representations for the data
(typically, transforming them to bit patterns of some kind) one can then
make use of distance-metrics and nearest-neighbor retrieval algorithms
(this is my current favorite; I have a knowledge-base interface
with all sorts of approximate-match reference built in). References:
Kanerva, Pentti, Self-Propagating Search, CSLI report 84-7 (now a book)
Geoffrey Hinton, Distributed Representations, CMU-CS-84-157 (also in PDP?)
Lots of work on information retrieval by semantic distance,
such as Gerald Salton's or the Connection-Machine implementation
described in Stanfill and Kahle, Parallel Free-Text Search...,
COMM ACM, Dec 1986
Other related work has appeared regarding trees and graphs:
the RETE algorithm for speedily finding productions whose conditions
are satisfied-- see any good algorithms text or
Forgy, RETE: A Fast Algorithm..., AI vol 19, 1982
K-D trees and other divide-and-conquer methods for speeding up searches
ex: Omohundro, Efficient Algorithms with Neural Network Behavior,
U. Ill. Report UIUCDCS-R-87-1131;
see also Preparata anmd Shamos, Computational Geometry,
finding patterns in networks, eg for simplifying constriant networks
ex: Gosling, Algebraic Constraints, CMU CS-83(?)-132
Spencer, Weighted Matching Algorithms, Stanford CS-87-1162
And of course there's stuff found under semantic network systems
explicitly, eg,
there was one in the Prospector system, see
Reboh, Knowledge Engineering Tools in Prospector..., SRI TN243, 1981
theres a desdcription of KODIAK and operations on its structures in
Norvig, Unified Theory of Text Understanding, UCB CSD 87-339
lots of the work at Yale (or extending out of it) deals implicitly
with the need to recognize patterns in large semantice networks
BORIS, IPP, UNIMEM, etc.
Kolodners CYRUS work (see Cog Sci, 1981?)
Patil, Causal Repr. of Acid-Base Diagnosis, MIT LCS TR-267, 1981
deals with the issues for translating between alternate network
representations (representing different levels of causal explanation)
or a generally neat article,
Pople, Heuristic Methods for imposing Structure..., in
Szolovitz, ed, AI in Medicine, 1982
Or classification algorithms (if one can find intelligent ways to
group items into classes, this forms a coarse metric for similarity
and might greatly reduce the work needed to find the most):
Shepard, Toward a Universal law of Generalization..., Science, 11 sept 87
Bobick, Natural Object Categorization, MIT AI TR 1001, 1987
Eleanor Rosch's work on how classification works in people
One could also look throught the analogy literature; there's clearly
a notion of structure-recognition and mapping there (Gentner, 1982)
Misc refererences I havent gotten around to digesting:
Cohen, A Powerful and Efficient Structural Pattern-Recognition System,
Art. Intell. 9, 1978
Purdom & Brown, Tree Matching and Simplification, Software
Practice&experience, Feb 1987
-----------------------------------------------------------------------
References i've recieved since the last posting of 7/87:
-----------------------------------------------------------------------
From: rada@mcs.nlm.nih.gov (Roy Rada CSB)
To: rjz%jasper@live-oak.lcs.mit.edu
Subject: matching
Roland,
I have done some work on matching of query and documents through
spreading activation in a semantic network. Some papers on the subject
are under review but the following are also relevant:
%A Roy Rada
%T Knowledge-Sparse and Knowledge-Rich Learning in Information Retrieval
%J Information Processing and Management
%D 1987
%P 195-210
%A Richard Forsyth
%A Roy Rada
%T Machine Learning: Expert Systems and Information Retrieval
%I Ellis Horwood
%C London
%D 1986
%A Roy Rada
%T Gradualness Facilitates Knowledge Refinement
%J IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 5
%D September 1985
%P 523-530
%A Hafedh Mili
%A Roy Rada
%T A Statistically Built Knowledge Base
%J Proceedings Expert Systems in Government Conference
%D Oct 1985
%I IEEE Computer Society Press
%P 457-463
My address is Roy Rada
National Library of Medicine
Bethesda, MD 20984
Roy
p.s. by the way, this is in response to your request on IRList.
-----------------------------------------------------------------
Date: Thu, 2 Jul 87 09:17:35 PDT
From: Michael Shafto <shafto@ames-aurora.arpa>
To: RJZ%JASPER@LIVE-OAK.LCS.MIT.EDU
Roland --
I saw your recent posting summarizing replies re: partial
structure-matching.
With respect to Mike Tanner's response, I would add that
Reggia's work is very good from the standpoint of being
fairly realistic and extremely well-grounded mathematically.
It is not yet clear to me exactly what the scope of Reggia's
work is. It started out being applied to medical diagnosis,
has now been extended to other types of diagnostic reasoning,
and (recently) Yun Peng and Jim Reggia have developed an
integrated framework for modeling causal and probabilistic
reasoning. Reggia is also working in the area of
neural network (connectionist) models, which provides a
whole other approach to partial matching.
With respect to Len Moskowitz's reply (recommending Lebowitz
and Kolodner), I would add the following: Essentially, analogical
reasoning is a kind of partial matching problem. Two lines
of research which specifically address analogy as partial
structure mapping are those of Jaime Carbonell (he worked on
the issue of partial plan-matching, retrieval, and adaptation)
and Ken Forbus/Dedre Gentner (they are working on analogical
reasoning in science, and have developed a metric for
goodness of analogical match, using Dempster-Shafer theory).
Carbonell is still at CMU, as far as I know. Forbus and
Genter are at the University of Illinois (Forbus in CS and
Gentner in Psych).
Mike Shafto
---------------------------------------------------------------
From: DCB.pa@Xerox.COM
Subject: Re: graph parsing
In-reply-to: <870618171750.4.RJZ@UBIK.PALLADIAN.COM>
To: Roland Zito-Wolf <RJZ%JASPER@LIVE-OAK.LCS.MIT.EDU>
Cc: DCB.pa@Xerox.COM
I don't have a copy of my TR in front of me, so I can't answer your
question about the complexity analysis right now. I can say, however,
that that complexity analysis is a total hack, and you could probably do
a better job yourself by following that line of reasoning for about an
hour or so. Also, that's a worst case analysis, and it assumes very
wasteful data structures, so I don't think it has much to say about how
any real implementation would run.
As far as approximate string-matching algorithms are concerned, you are
probably best off looking in something like SigGraph back issues or
those of some pattern matching journal. Keep in mind, however, that
pattern matchers don't look very much like parsers, so it's not clear
that you could generalized them to graphs in at all a similar way.
Sorry I couldn't be more help. Good luck with whatever.
dan
-------------------------------------------------------------------
Date: Thu, 2 Jul 87 13:18 EDT
From: William J. Rapaport <rapaport%cs.buffalo.edu@RELAY.CS.NET>
Subject: graph matching algorithms
To: nl-kr@CS.ROCHESTER.EDU
Backward-References: The message of 13 Jul 87 15:51 EDT from nl-kr-request@cs.rochester.edu,
<8707132042.AA12914@castor.cs.rochester.edu>
We have a couple of graph-matching algorithms for the SNePS semantic
network processing system. Relevant papers are:
Shapiro, Stuart C., & Rapaport, William J. (1987), "SNePS Considered as
a Fully Intensional Propositional Semantic Network," in G. McCalla and
N. Cercone (eds.), The Knowledge Frontier: Essays in the Representation
of Knowledge (New York: Springer-Verlag): 262-315; earlier version
preprinted as Technical Report No. 85-15 (Buffalo: SUNY Buffalo Dept.
of Computer Science, 1985).
Saks, Victor (1985), "A Matcher of Intensional Semantic Networks," SNeRG
Technical Note No. 12 (Buffalo: SUNY Buffalo Dept. of Computer
Science).
Copies of these papers and a complete bibliography are available by
writing Ms. Lynda Spahr, Dept. of Computer Science, SUNY Buffalo,
Buffalo, NY 14260; spahr@buffalo.csnet; spahr@sunybcs.bitnet.
William J. Rapaport
Assistant Professor
Dept. of Computer Science, SUNY Buffalo, Buffalo, NY 14260
(716) 636-3193, 3180
uucp: ..!{allegra,decvax,watmath,rocksanne}!sunybcs!rapaport
csnet: rapaport@buffalo.csnet
bitnet: rapaport@sunybcs.bitnet
Roland J. Zito-wolf (aka Roy)
Dept. of Computer Science, Ford Hall Room 121
Brandeis University
Waltham, Mass 02254-9110
617-736-2728
RJZ@CS.BRANDEIS.EDU or
RJZ%CS.BRANDEIS.EDU@RELAY.CS.NET
------------------------------
Date: Thu, 3 Nov 88 11:27 EST
From: Siping Liu <siping@b.cs.wvu.wvnet.edu>
Subject: Inherit through net
In frame knowledge representation systems, knowledge
can be inherited through the tree-style world hierarchies.
i.e., each world has only one parent world.
The question is: if the intersection of the confined problem
spaces for two (or more) brother worlds is not empty, why can not
they have a common child world with the intersection as its
problem space ?
BTW, the question is raised when I am thinking how to fit ATMS
(Assumption-based Truth Maintenance System) into a frame system.
------------------------------
Date: Sun, 6 Nov 88 22:51 EST
From: Michael R Hall <sword!gamma!pyuxp!nvuxj!nvuxl!nvuxh!hall@faline.bellcore.com>
Subject: Re: Inherit through net
In a previous article, Siping Liu writes:
>In frame knowledge representation systems, knowledge
>can be inherited through the tree-style world hierarchies.
>i.e., each world has only one parent world.
>
>The question is:
[Why not allow multiple parents?]
Sure, you can have multiple parents in some frame-inheritence
implementations. KEE lets you do it. You should be able to find
some literature on the research problems associated with doing this
type of inheritence gracefully.
--
Michael R. Hall | Bell Communications Research
"I'm just a symptom of the moral decay that's | nvuxh!hall@bellcore.COM
gnawing at the heart of the country" -The The | bellcore!nvuxh!hall
------------------------------
Date: Tue, 8 Nov 88 13:37 EST
From: Graeme Hirst <gh@ai.toronto.edu>
Subject: Visiting position in Natural Language Understanding
VISITING POSITION IN NATURAL LANGUAGE UNDERSTANDING
UNIVERSITY OF TORONTO
ARTIFICIAL INTELLIGENCE GROUP
(DEPARTMENT OF COMPUTER SCIENCE)
A one-year visiting position, for a post-doc or more senior person, is
available for 1989-90 in the University of Toronto A.I. group in the
area of natural language understanding and computational linguistics.
The visitor would carry a 50% teaching load (one half-course per
semester), participate in the research group activities, and possibly
supervise MSc theses.
The Toronto AI group includes 7.5 faculty, 2 research scientists, and
approximately 40 graduate students. The natural language subgroup
includes one faculty member (Graeme Hirst) and about ten graduate
students and associates.
For more information, contact Graeme Hirst, preferably by e-mail.
Graeme Hirst
Department of Computer Science
University of Toronto
Toronto, CANADA M5S 1A4
E-mail: gh@ai.toronto.edu or .ca
gh@ai.utoronto.bitnet
Phone: 416-978-8747 (Tues, Thurs, Fri) or 416-284-3360 (Mon and Wed)
--
\\\\ Graeme Hirst University of Toronto Computer Science Department
//// uunet!utai!gh / gh@ai.toronto.edu / 416-978-8747
------------------------------
Date: Sun, 13 Nov 88 11:28 EST
From: Jeff Elman <elman@amos.ling.ucsd.edu>
Subject: UCSD PhD. program in Cognitive Science
GRADUATE STUDIES IN COGNITIVE SCIENCE
UNIVERSITY OF CALIFORNIA, SAN DIEGO
Two graduate programs in cognitive science are offered at UCSD.
The Department of Cognitive Science will offer a PhD in Cognitive
Science beginning fall quarter, 1989. The core curriculum will
emphasize the theoretical and empirical study of cognitive
phenomena, the neurological basis of cognitive processes, and
computational modeling.
Application deadline for fall quarter, 1989: January 15, 1989.
GRE: Verbal, quantitative, and analytical sections required.
For application materials and more information contact Lynne
Keith, Department of Cognitive Science, C-015, UC San Diego, La
Jolla, CA 92093 (619-534-6771) (email: lkeith@ucsd.edu).
The Group in Cognitive Science will continue to offer a joint PhD
program wherein a student enters a home department affiliated
with cognitive science (Anthropology, Computer Science,
Linguistics, Neurosciences, Sociology, Philosophy, or Psychology)
and, after a year of study in that department, applies to the
Interdisciplinary Program in Cognitive Science as well. This
program leads to a PhD in the home department and Cognitive
Science. Contact the relevant home department for application
and admission information.
------------------------------
Date: Mon, 14 Nov 88 14:48 EST
From: Steve Pinker <steve@psyche.mit.edu>
Subject: Assistant Professor position at MIT
November 8, 1988
JOB ANNOUNCEMENT
The Department of Brain and Cognitive Sciences (formerly the
Department of Psychology) of the Massachusetts Institute of
Technology is seeking applicants for a nontenured, tenure-track
position in Cognitive Science, with a preferred specialization in
psycholinguistics, reasoning, or knowledge representation. The
candidate must show promise of developing a distinguished
research program, preferably one that combines human
experimentation with computational modeling or formal analysis,
and must be a skilled teacher. He or she will be expected to
participate in department's educational programs in cognitive
science at the undergraduate and graduate levels, including
supervising students' experimental research and offering courses
in Cognitive Science or Psycholinguistics.
Applications must include a brief cover letter stating the
candidate's research and teaching interests, a resume, and at
least three letters of recommendation, which must arrive by
January 1, 1989. Address applications to:
Cognitive Science Search Committee
Attn: Steven Pinker, Chair
E10-018
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
------------------------------
Date: Tue, 15 Nov 88 11:26 EST
From: Henry J. Hamburger <hhamburg@note.nsf.gov>
Subject: NSF Program in Knowledge Models and Cognitive Systems
NATIONAL SCIENCE FOUNDATION
---------------------------
PROGRAM in
----------
KNOWLEDGE MODELS and COGNITIVE SYSTEMS
--------------------------------------
Knowledge Models and Cognitive Systems is a relatively new name
at NSF, but the Program has significant continuity with earlier
related programs. This holds for its scientific subject matter
and also with regard to its researchers, who come principally
from computer science and the cognitive sciences, each of these
emphatically including important parts of artificial intelligence.
Many such individuals are also interested in areas supported by
other NSF programs, especially in this division -- the Division
of Information, Robotics and Intelligent Systems (IRIS) -- and in
the Division of Behavioral and Neural Sciences.
This unofficial message has two parts. The first is a top-down
description of the major areas of current Program support. There
follows a list of some particular topics in which there is strong
current activity in the Program and/or perceived future
opportunity. Anyone needing further information can contact the
Program Director, Henry Hamburger, who is also the sender of this
item. Please use e-mail if you can: hhamburg@b.nsf.gov or else
phone: 202-357-9569. To get a copy of the Summary of Awards for
this division (IRIS), call 202-357-9572
Many of you will be hearing from me with requests to review
proposals. To be sure they are of interest to you, feel free to
send me a list of topics or subfields.
MAJOR AREAS of CURRENT SUPPORT
------------------------------
The Program in Knowledge Models and Cognitive Systems supports
research fundamental to the general understanding of knowledge
and cognition, whether in humans, computers or, in principle,
other entities. Major areas currently receiving support include
(i) formal models of knowledge and information, (ii) natural
language processing and (iii) cognitive systems. Each of these
areas is described and subcategorized below.
Applicants do not classify their proposals in any official way.
Indeed their work may be relevant to two or all three of the
categories (or conceivably to none of them). In particular, it
is recognized that language is intertwined with (or part of)
cognition and that formality is a matter of degree. For work
that falls only partly within the program, the program director
may conduct the evaluation jointly with another program, within
or outside the division. Descriptions of the three areas follow.
FORMAL MODELS of KNOWLEDGE and INFORMATION:
-------------------------------------------
Recent work supported under the category Formal Models of
Knowledge and Information divides into formal models of three
things: (i) knowledge, (ii) information, and (iii) imperfections
in the two. In each case, the models may encompass both
representation and manipulation. For example, formal models of
both knowledge representation and inference are part of the
knowledge area.
The distinction between knowledge and information is that a piece
of knowledge tends to be more structured and/or comprehensive
than a piece of information. Imperfections may include
uncertainty, vagueness, incompleteness and abductive rules. Many
investigations contribute to two or all three categories, yet
emphasize one.
COGNITIVE SYSTEMS
-----------------
Four recognized areas currently receive support within Cognitive
Systems: (i) knowledge representation and inference, (ii)
highly parallel approaches, (iii) machine learning, and (iv)
computational characterization of human cognition.
The first area is characterized by symbolic representations and a
high degree of structure imposed by the programmer, in an attempt
to represent complex entities and carry out complex tasks
involving planning and reasoning. The second area may have
similar long-term goals but takes a very different approach. It
includes studies based on a high degree of parallelism among
relatively simple processing units connected according to various
patterns. The third area, machine learning, has emerged as a
distinct area of study, though the choice between symbolic and
connectionist approaches is clearly relevant. In all of the
first three areas, the research may be informed to a greater or
lesser degree by scientific knowledge of the nature of high-
level human cognition. Characterizing such knowledge in
computational form is the objective of the fourth area.
NATURAL LANGUAGE PROCESSING
---------------------------
Recent work supported under the category Natural Language
Processing is in three overlapping areas: (i) computational
aspects of syntax, semantics and the lexicon, (ii) discourse,
dialog and generation, and (iii) systems issues. The distinction
between the first two often involves such intersentential
concerns as topic, plan, and situation. Systems issues include
the interaction and unified treatment of various kinds of
modules.
TOPICS of STRONG CURRENT ACTIVITY and
-------------------------------------
OPPORTUNITY for FUTURE RESEARCH
-------------------------------
Comments on this list are welcome. It has no official status,
is subject to change, and, most important, is intended to be
suggestive, not prescriptive. The astute reader will notice that
many of these topics transcend the neat categorization above.
Reasoning and planning in the face of
imperfect information and a changing world
- reasoning about reasoning itself: the time
and resources taken, and the consequences
- use and formal understanding of
temporal and nonmonotonic logic
- integration of numerical and symbolic approaches
to uncertainty, imprecision and justification
- multi-agent planning, reasoning,
communication and coordination
Interplay of human and computational languages
- commonalities in the semantic formalisms
for human and computer languages
- extending knowledge representation systems to
support formal principles of human language
- principles of extended dialog between humans
and complex software systems, including
those of the new computational sciences
Machine Learning of Classification,
Problem-Solving and Scientific Laws
- formal analysis of what features and parameter
settings of both method and domain are
responsible for successes.
- reconciling and combining the benefits of
connectionist, genetic and symbolic approaches
- evaluating the relevance to learning of AI
tools: planning, search, and learning itself
------------------------------
Date: Sat, 19 Nov 88 14:34 EST
Department of Linguistics
University of Delaware
46 E. Delaware
Newark, DE 19716 U.S.A.
(302) 451-6808
EMAIL: cole@vax1.acs.udel.edu, AXR00786@UDACSVM.Bitnet
The following pages provide information on faculty openings and
possibilities for graduate study and financial aid at the Department of
Linguistics of the University of Delaware. We would appreciate your posting
this information and passing it on to interested students and colleagues.
With regard to graduate study, the Department encourages applications from
students with backgrounds in computer science, psychology, mathmatics etc.,
as well as in linguistics itself.
Please contact us if you desire additional information.
1) OPPORTUNITIES FOR GRADUATE STUDY IN LINGUISTICS
Dear Colleague,
I am writing to ask your assistance in identifying superior students
with an interest in linguistics who might be appropriate candudates for
financial aid in our growing doctoral program. The Department of Linguistics
has been selected by the University of Delaware administration for growth
and development. Over the last year, the Department faculty has grown
from nine to eleven, and we anticipate expansion to sixteen or more over
the next several years. The Department has traditionally been strong in a
number of areas of applied linguistics, especially language acquisition, and
L2/ESL pedagogy and testing. In addition to these areas, the Department is
now undergoing major expansion in theoretical linguistics, especially syntax
and phonology. A number of faculty members have strong interests in the
application of current linguist theory to the description of less commonly
taught languages like Chinese, Japanese and Quechua. There is also
considerable interest in the examination of theoretical constructs from
formal syntax in both first and second language acquisition.
The Department is interested in recruiting a number of first-rate
graduate students for the coming year. These students need not have an
extensive undergraduate background in linguistics, but they should have a
strong interest in natural language, and have the capability to develop into
serious researchers. We expect to be in a position to offer quite a generous
program of financial aid. The aid available includes fellowships, research
assistantships and teaching assistantships. The stipends for financial aid
range from about $7450 to $8200 plus tution waiver. A student entering the
program with a B.A., will usually receive five years of financial aid if he or
she is making satisfactor progress toward the Ph.D. Most students admitted
to the program will be awarded financial aid.
We would appreciate your help in finding truly excellent students for
our program. We are in the process of arranging financing for visits to our
campus of especially promising students. We hope that you will call or write
to us if you have students that you would like to recommend for our
program.
Thank you for your assistance. If you have any questions, please let
me know.
Sincerely,
Peter Cole
Chair
2) LINGUISTICS JOB INFORMATION
Dear Colleague:
I am writing to tell you about the Department of Linguistics at the
University of Delaware, and to ask your assistance in identifying candidates
for faculty positions in our Department. The Department of Linguistics has
been selected by the University of Delaware administration for growth and
development. The University as a whole is undergrowing a major expansion
of its graduate and research programs. Over the last year, the Department
faculty has grown from nine to twelve (including one joint appointment), and
we anticipate expansion to seventeen or more over the next several years.
The Department has traditionally been strong in a number of areas of
applied linguistics, especially language acquisition, and L2/ESL pedagogy and
testing. In addition to these areas, the Department is now undergoing
MAJOR expansion in theoretical linguistics, especially syntax, semantics and
phonology. There are now three syntacticians teaching in the Department
and one phonologist. A number of faculty members have strong interests in
the application of current linguist theory to the description of less commonly
taught languages like Chinese, Japanese and Quechua. There is also
considerable interest in the examination of theoretical constructs from
formal syntax in both first and second language acquisition.
We plan to recruit one or more faculty members for September 1989.
A copy of our advertisement in enclosed. The Department is interested in
recruiting linguists with superlative records in both research and teaching.
We will refrain from making any appointment if we cannot identify an
appropriate candidate. The areas of specialization in which we have greatest
interest are phonology, syntax, formal semantics and morphology.
Specialization in an East Asian language is a desirable additional
qualification. Applications are encouraged from both junior and senior
applicants, but tenured appointments and appointment above the level of
Assistant Professor will require clear justification in terms of the
achievements of the candidate. Applications from minority members and
women are especially welcome.
I hope you assist us in identifying exceptional linguists with interests
one or more of the specializations we have advertised. For first
consideration, applications should be received by January 15, 1988, and
should include a C.V., a brief statement of current and projected research
interests, and the names, addresses and telephone numbers of at least three
referees, as well as copies of publications. Candidates should also indicate if
they plan to attend the 1988 Annual Meeting of the Linguistic Society of
America. Materials should be sent to Professor Peter Cole, Chair, Department
of Linguistics, University of Delaware, 46 E. Delaware, Newark, D.E. 19716.
Please post our advertisement and draw the attention of your
colleagues to these positions.
Sincerely yours,
Peter Cole
Professor and Chair
Job Announcement
The Department of Linguistics of the University of Delaware anticipates one
or more tenure track openings in the following areas of specialization:
phonology, formal semantics, syntax and morphology. Specialization in an
East Asian language is a desirable additional qualification. The Department
is interested in applicants with superlative records in both research and
teaching. Applications are encouraged from both junior and senior
applicants, but appointment above the level of Assistant Professor will
require clear justification in terms of the achievements of the candidate.
For first consideration, applications should be received by January
15, 1989, and should include a C.V., a brief statement of current and
projected research interests, and the names, addresses and telephone
numbers of at least three referees, as well as copies of publications.
Candidates should also indicate if they plan to attend the 1988 Annual
Meeting of the Linguistic Society of America. Materials should be sent to
Professor Peter Cole, Chair, Department of Linguistics, University of
Delaware, 46 E. Delaware Avenue, Newark, D.E. 19716.
The University of Delaware is an equal opportunity/affirmative employer.
Applications from minority candidates and women are strongly encouraged.
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End of NL-KR Digest
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