linguistlist.org>
Editor for this issue: Marie Klopfenstein <marielinguistlist.org>
noos.fr>Call for papers for the special issue of the TAL journal Title: ELECTRONIC DICTIONARIES, FOR MEN, MACHINES OR FOR BOTH? Submission deadline: 15 December 2002 Guest Editors: Michael Zock (CNRS, LIMSI) & John Carroll (University of Sussex). http://www.atala.org/tal/appel-dictionnaires-electroniques.html - --------------------------------------------------------------------------= - Dictionaries are the backbone of any NLP system. Understanding and producing text, translation, summarisation, dialog, indexing or finding information in a document all require lexical competency represented in computers as a lexical resource / dictionary. A good dictionary is characterized by the following features: broad coverage (number of entries), rich annotation (a lot of information associated with each entry) and ease of access of the information. If electronic dictionaries compare favourably with paper dictionaries (size, ease of access), they are still far from perfect in particular with regard to content and access. Coverage is certainly not the only criterion for evaluating a dictionary, because, what is a large dictionary good for if the data is not easily accessible? - --------------------------------------------------------------------------= - The goal of this special issue is to discuss challenges inherent to the building and use of electronic dictionaries and approaches and techniques to address them. We welcome work on any of the following issues: o the problem of building a dictionary (method, know-how); o types of information to be stored in a dictionary; o representation, structuring (indexing) and visualisation of the data; o the problem of accessing information (aids for navigation, interface, strategies); o acquisition of lexical data (corpus), reuse of existing data; o coherency checking; o problems related to multilinguality; o possibilities given to the user or lexicographer to edit entries (annotation, updating); o the usage of dictionaries by people (learning/teaching; writing) and by machines (NLP). Given the wide spectrum of needs we welcome work from any of the following perspectives, linguistics, computer science, psycholinguistics, language learning, ergonomics, etc. provided the contibution contains a computational element. Reviewers - --------------------------------------------------------------------------= - - Christian Boitet (GETA, Grenoble) - Nicoletta Calzolari (CNR, Pisa) - Christiane Fellbaum (University of Princeton) - Charles Fillmore (University of Berkeley) - Ulrich Heid (IMS-CL, University of Stuttgart) - Jean-Marie Pierrel (ATILF, Nancy) - Alain Polguere (University of Montreal) - Thiery Selva (GRELEP, K.U.Leuven, Belgium) - Gilles Serasset (GETA, Grenoble) - Monique Slodzian (CRIM, INALCO, Paris) - Patrick St. Dizier (University of Toulouse) - Jean Veronis (University of Aix en Provence) - Piek Vossen (Irion Technologies, Delft, The Netherlands) - Leo Wanner (University of Stuttgart) Format - --------------------------------------------------------------------------= - Papers (25 pages maximum) may be submitted in Word, Postscript or PDF. The Hermes style sheets are available at Lavoisier and from the TAL journal web site (http://www.atala.org/tal/hermes/cons_actes.htm). Language - --------------------------------------------------------------------------= - The papers may be written either in French or in English (non-French speaking authors only) Schedule - --------------------------------------------------------------------------= - The submission deadline is 15 December 2002. People intending to submit a paper should contact Michael Zock (zockMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue
limsi.fr>******************************************************************************************* Second call for papers (TAL journal ): Deadline Extension to October 14, 2002 Automated Learning of Language Models Deadline for submission : October 14, 2002 Issue coordinated by Mich�le Jardino (CNRS, LIMSI), and Marc El-Beze (LIA, University of Avignon) . - - Language Models (LM) play a crucial role in the working of Automated Natural Language Processing systems, when real-life problems (often very large ones) are being dealt with. Instances are Speech Recognition, Machine Translation and Information Retrieval. If we want these systems to adapt to new applications, or to follow the evolution in user behaviour, we need to automatize the learning of parameters in the models we use. Adaptation should occur in advance or in real time. Some applications do not allow us to build an adequate corpus, either from a quantitative or qualitative point of view. The gathering of learning data is made easier by the richness of Web resources, but in that huge mass, we have to effectively separate the wheat from the chaff. When asked about the optimal size for a learning corpus, are we satisfied to answer "The bigger, the better"? Rather than training one LM on a gigantic learning corpus, would it not be advisable to fragment this corpus into linguistically coherent segments, and learn several language models, whose scores might be combined when doing the test (model mixture)? In the case of n-gram models, which is the optimal value for n? Should it be fixed or variable? A larger value allows us to capture linguistic constraints over a context which goes beyond the mere two preceding words of the classic trigram. However, increasing n threatens us with serious coverage problems. Which is the best trade-off between these two opposite constraints? How can we smooth models in order to approximate phenomena that have not been learned? Which alternatives are to be chosen, using which more general information (lesser-order n-grams, n-classes?) Beyond the traditional opposition between numerical and knowledge-based approaches, there is a consensus about the introduction of rules into stochastic models, or probability into grammars, hoping to get the best of both strategies. Hybrid models can be conceived in several ways, depending on which choices are made regarding both of their sides, and also, the place where coupling occurs. Because of discrepancies between the language a grammar generates, and actually observed syntagms, some researchers decided to reverse the situation and derive the grammar from observed facts. However, this method yields disappointing results, since it does not perform any better than n -gram methods, and is perhaps inferior. Shouldn't we introduce here a good deal of supervision, if we want to reach this goal? Topics (non-exhaustive list) ------------------------------------------------------------------------ In this special issue, we would like to publish either innovative papers, or surveys and prospective essays dealing with Language Models (LM), Automated Learning of their parameters, and covering one of following subtopics: * Language Models and Resources: o determination of the adequate lexicon o determination of the adequate corpus * Topical Models * LM with fixed or variable history * Probabilistic Grammars * Grammatical Inference * Hybrid Language Models * Static and dynamic adaptation of LMs * Dealing with the Unknown o Modelling words which do not belong to the vocabulary o Methods for smoothing LMs * Supervised and unsupervised learning of LMs o Automated classification of basic units o Introducing linguistic knowledge into LMs * Methods for LM learning o EM, MMI, others? * Evaluation of Language Models * Complexity and LM theory * Applications: - Speech Recognition - Machine Translation - Information Retrieval Format ------------------------------------------------------------------------ Papers (25 pages maximum) are to be submitted in Word ou LaTeX. Style sheets are available at HERMES : < http://www.hermes-science.com/ >. Language ------------------------------------------------------------------------ Articles can be written either in French or in English, but English will be accepted from non-French speaking authors only. Deadlines ------------------------------------------------------------------------ Submission deadline is October 7, 2002. Articles will be reviewed by a member of the editorial board and two external reviewers designed by the editors of this issue. Decisions of the editorial board and referees' report will be transmitted to the authors before November 20, 2002. The final version of the accepted papers will be required by February 20, 2003. Publication is planned during the spring of 2003. Submission ------------------------------------------------------------------------ Submissions must be sent electronically to: tal.mlMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue