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Category:   E-CFP
Subject:   Word Sense Disambiguation, Special Issue
From:   Mark Stevenson
Email:   M.Stevenson_(on)_dcs.shef.ac.uk
Date received:   17 Jan 2003
Deadline:   01 Oct 2003

Call for Papers: Journal of Computer Speech and Language Special Issue on WORD SENSE DISAMBIGUATION Guest editors: Judita Preiss, Judita.Preiss_(on)_cl.cam.ac.uk Mark Stevenson, M.Stevenson_(on)_dcs.shef.ac.uk The process of automatically determining the meanings of words, word sense disambiguation (WSD), is an important stage in language understanding. It has been shown to be useful for many natural language processing applications including machine translation, information retrieval (mono- and cross-lingual), corpus analysis, summarization and document navigation. The usefulness of WSD has been acknowledged since the 1950's and the field has recently enjoyed a resurgence of interest including the creation of SENSEVAL, an evaluation exercise allowing a basic precision/recall comparison of participating systems, which has been run twice to date. The current availability of large corpora and powerful computing resources has made the exploration of machine learning and statistical methods possible. This is in contrast to the majority of early approaches which relied on hand-crafted disambiguation rules. This special issue of Computer Speech and Language, due for publication in 2004, is intended to describe the current state of the art in word sense disambiguation. Papers are invited on all aspects of WSD research, and especially on: * Combinations of methods and knowledge sources. Which methods or knowledge sources complement each other and which provide similar disambiguation information? How should they be combined? Do better disambiguation results justify the extra cost of producing systems which combine multiple techniques or use multiple knowledge sources? Can any method or knowledge source be determined to be better or worse than another? * Evaluation of WSD systems. Which metrics are most informative and would new ones be useful? Can WSD be evaluated in terms of the effect it has on another language processing task, for example parsing? Can evaluations using different data sets (corpora and lexical resources) be compared? Can the cost of producing evaluation data be reduced through the use of automatic methods? * Sense distinctions and sense inventories. How do these affect WSD? How does the granularity of the lexicon affect the difficulty of the WSD task? Are some types of sense distinction difficult to distinguish in text? What can be gained from combining sense inventories and how can this be done? * The effect of WSD on applications. To what extent does WSD help applications such as machine translation or text retrieval? What kind of disambiguation is most useful for these applications? What is the effect when the disambiguation algorithm makes mistakes? * Minimising the need for hand-tagged data. Hand-tagged text is expensive and difficult to obtain while un-tagged text is plentiful and, effectively, limitless. What techniques can be used to make use of un-tagged text, would weakly/semi-supervised learning algorithms be useful? What use can be made of parallel text? Can un-tagged text be made as useful as disambiguated text? Submission Information Initial Submission Date: 1 October 2003 All submissions will be subject to the normal peer review process for this journal. Submissions in electronic form (PDF) are strongly preferred and must conform to the Computer Speech and Language specifications, which are available at: http://authors.elsevier.c om/journal/csl Any initial queries, should be addressed to Judita.Preiss_(on)_cl.cam.ac.uk

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