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cs.rutgers.edu>Building Educational Applications Using Natural Language Processing HLT/NAACL 2003 Workshop May 31, 2003 Edmonton, Canada http://www.etstechnologies.com/NAACL Overview There is an increased use of NLP-based educational applications for both large-scale assessment and classroom instruction. This has occurred for two primary reasons. First, there has been a significant increase in the availability of computers in schools, from elementary school to the university. Second, there has been notable development in computer-based educational applications that incorporate advanced methods in NLP that can be used to evaluate students' work. Educational applications have been developed across a variety of subject domains in automated evaluation of free-responses and intelligent tutoring. To date, these two research areas have remained autonomous. We hope that this workshop will facilitate communication between researchers who work on all types of instructional applications, for K-12, undergraduate, and graduate school. Since most of this work in NLP-based educational applications is text-based, we are especially interested in any work of this type that incorporates speech processing and other input/output modalities. We wish to expose the NLP research community to these technologies with the hope that they may see novel opportunities for use of their tools in an educational application. Invited Speaker: Thomas Landauer, University of Colorado, Boulder, and Knowledge Analysis Technologies (KAT) Call for Papers We are especially interested in submissions including, but not limited to: * Speech-based tools for educational technology * Innovative text analysis for evaluation of student writing with regard to: a) general writing quality, or b) accuracy of content for domain-specific responses * Text analysis methods to handle particular writing genres, such as legal or business writing, or creative aspects of writing * Intelligent tutoring systems that incorporate state-of-the-art NLP methods to evaluate response content, using either text- or speech-based analyses * Dialogue systems in education * understanding student input * generating the tutors' feedback * evaluation * Evaluation of NLP-based tools for education * Use of student response databases (text or speech) for tool building * Content-based scoring Important Dates: Paper submission deadline: Mar 17 Notification of acceptance for papers: Mar 31 Camera ready papers due: Apr 8 Workshop date: May 31 Organizers Jill Burstein, Educational Testing Service (jbursteinMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue
cs.rutgers.edu>______________________________________________________________ 2nd CALL FOR PAPERS ACL-2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment 12 July 2003, Sapporo, Japan ______________________________________________________________ WEBSITES Workshop website: http://www.cl.cam.ac.uk/users/alk23/mwe/mwe.html ACL website: http://www.ec-inc.co.jp/ACL2003/ WORKSHOP DESCRIPTION Multiword expressions (MWEs) include a large range of linguistic phenomenon, such as phrasal verbs (e.g. "add up"), nominal compounds (e.g. "telephone box"), and institutionalized phrases (e.g. "salt and pepper"), and they can be syntactically and/or semantically idiosyncratic in nature. MWEs are used frequently in everyday language, usually to express precisely ideas and concepts that cannot be compressed into a single word. A considerable amount of research has been devoted to this subject, both in terms of theory and practice, but despite increasing interest in idiomaticity within linguistic research, there is still a gap between the needs of NLP and the descriptive tradition of linguistics. Owing to the lack of adequate resources to identify and treat MWEs properly, they pose a real challenge for NLP. Most real-world applications tend to ignore MWEs or address them simply by listing. However, it is clear that successful applications will need to be able to identify and treat them appropriately. This particularly applies to the many applications which require some degree of semantic processing (e.g. machine translation, question-answering, summarisation, generation). In recent years there has been a growing awareness in the NLP community of the problems that MWEs pose and the need for their robust handling. A considerable amount of research has been conducted in this area, some within large research projects dedicated to MWEs (e.g. the Multiword Expression Project). There is also a growing interest in MWEs in projects focused on tasks such as parsing (e.g. Robust Accurate Statistical Parsing (RASP)) and word sense disambiguation (e.g. MEANING - Developing Multilingual Web-scale Language Technologies) which are required by real-world applications. Previous workshops on MWEs have focused on certain MWE types, notably collocations, terminology and named entities. There are, however, further subtypes of MWEs, which are highly relevant for NLP tasks but which have not to date received specific attention. One example are lexicalised (non- or semi-compositional) MWEs which raise specific issues for applications which require semantic interpretation. TARGET AUDIENCE This workshop is intended to bring together NLP researchers working on all areas of MWEs. The objective is to summarise what has been achieved in the area, to establish common themes between different approaches, and to discuss future trends, with particular emphasis on addressing the problems that different MWE (sub)types pose for real-world NLP applications. AREAS OF INTEREST Papers are invited on, but not limited to, the following topics: * Theoretical research on MWEs * MWE taxonomies, classifications and databases * Corpus based analysis of MWEs * Cross-lingual analysis of MWE types, use, and behaviour * Methods for identification and extraction of MWEs (machine learning, statistical, example- or rule-based, or hybrid) * Evaluation of MWE extraction methods * Integration of MWE data into grammars and NLP applications (e.g. machine translation and generation) * Problems MWEs (or MWE types) pose for NLP applications and solutions proposed Papers can cover one or more of these areas. SUBMISSION INFORMATION Papers should be submitted electronically in Postscript or PDF format to: mweMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue