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SECOND ANNOUNCEMENT AND CALL FOR PAPERS
Workshop
Beyond Named Entity Recognition
Semantic labelling for NLP tasks
URL: http://ai-nlp.info.uniroma2.it/ws_lrec04/
Centro Cultural de Belem
LISBON, Portugal
25th may 2004
In Association with
4th INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
LREC2004
Main conference 26-27-28 May 2004
Motivation and Aims
Although it is generally assumed that improvements in language processing
will be made through the integration of linguistic information and
statistical techniques, the reality is that language is very diverse and
looking for specific patterns of words that repeat enough to be
statistically significant tends not to be a very fruitful task: sequences
longer than three words are not generally repeated often enough to be
statistically significant. At the same time, the identification of named
entities: Names, dates, places, organizations etc., has proved to be avery
useful preliminary task in many natural language processing systems are
interested in pursuing approaches which extend this notion by identifying
and labeling other semantic information in a text, in such as way as to
allow repeatable semantic patterns to emerge. Our interest is in attacking
the data sparseness problem by exploring ways to collapse (semantically)
related phrases which are expressed by different word sequences.
As this seems closely related to previously proposed class-based language
models (see for example Brown et al. 90 in Computational Linguistics), it
is distinguished because the empirical notion of classes used in the
previous work (e.g. classes made up of collocationally similar words) are
replaced by semantically justified sets.
Notice how Name Entity (NE) tagging and Word Sense Disambiguation (WSD)
represent, in terms of granularity and representational complexity, two
extremes of a single general problem: semantic disambiguation. Semantic
disambiguation serves thus the purpose of improving the generalization
power of statistical models. One of the questions here is how to determine
a suitable level of clustering (for NE identification and for WSD) that
would lead to high accuracy and to performance improvement by obtained
statistical models.
Reason of Interest
It is to be noticed that a set of independent research work focused
recently on the statistical treatment of semantic phenomena (e.g. WordNet
navigation as a stochastic process, as studied in Light and Abney or in
Ciaramita & Johnson) highly correlates with the research program proposed
above.
The workshop will represent a forum where experience from lexical semantics
and statistical learning will be presented and fruitful discussion among
researchers in both fields will be promoted. The workshop is expected to
attract researchers and practitioners from a range of areas as well as
developers of large scale semantic resources who are interested in
effective methods of semantic labeling.
Topics (to be addressed in the workshop include, but are not limited to)
* Methods for lexical - semantic annotation of corpora
* Methods and Standards for lexical semantic representation of dictionary
information
* Lexico-semantic taxonomies
* Existing sources of classification: dictionaries, thesauri and
computerized ontologies
* Corpus-driven methods for semantic disambiguation
* Feature selection for semantic disambiguation
* Lexico-semantic tagging of very large corpora
* Algorithms and methods for disambiguation of semantic phenomena
* Statistical learning models and their applications to semantic labeling
* Computational learning frameworks for Natural Language Learning
* Semi-supervised and unsupervised statistical semantic disambiguation
* Evaluation of semantic disambiguation
Workshop format
The workshop will be a half-day event with position statements from invited
speakers (half an hour each) with two hours for 4-6 presentations of
scientific papers. Submissions are intended to present works in progress
and more completed works which fall within the scope defined by the topics
listed above. A final 1 hour open discussion among all the workshop
participants will be moderated by the organizers. In order to stimulate an
interesting general discussion each member of the program committee will be
invited to submit a position statement of max. 1000 words.
Submission
Participants are invited to submit an extended abstract of max. 3500 words
concerning one or more of the topics of interest. Each accepted paper
receives a slot of 25 minutes for presentation (15 minutes talk and 10
minutes for discussion). Each submission should show: title; author(s);
affiliation(s); and contact author's e-mail address, postal address,
telephone and fax numbers. Submissions must be sent electronically in PDF
to the following address:
Roberto Basili
Dept. of Computer Science, Systems and Management
University of Roma Tor Vergata
e-mail: basili@info.uniroma2.it
Proceedings and Publications
Proceedings of the workshop will be printed by the LREC Local Organising
Committee.
The <http://www.elsevier.com/locate/issn/0885-2308>Computer, Speech and
Language journal will dedicate to the workshop topics a Special Issue on
Semantic tagging/labelling for NLP tasks. Relevant papers submitted to the
workshop will be selected to appear in that special issue.
Important dates
Extended abstract submission (max. 3500 words): 2nd of February 2004
Notification of acceptance: 5th of March 2004
Preliminary Program: 10th of March 2004
Submission of the final version of paper: 20th of April 2004
Workshop: 25th May 2004
Organising Committee
Louise Guthrie - University of Sheffield, UK
Roberto Basili - University of Rome, Tor Vergata, Italy
Eva Hajicova - Charles University, Czech Republic
Frederick Jelinek - Johns Hopkins University, Maryland, USA
Further Information
For any information related to the organization, please contact:
Roberto Basili
e-mail: basili@info.uniroma2.it
Dept. of Computer Science, Systems and Management
University of Roma Tor Vergata
Via di Tor Vergata
00133 Roma (ITALY)
tel: +39 06 72597391
fax: +39 06 72597460
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