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. ------------------------------ End of NL-KR Digest *******************