20.1609, Calls: Semantics,Computational Ling,Text/Corpus Ling/Singapore

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LINGUIST List: Vol-20-1609. Mon Apr 27 2009. ISSN: 1068 - 4875.

Subject: 20.1609, Calls: Semantics,Computational Ling,Text/Corpus Ling/Singapore

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1)
Date: 26-Apr-2009
From: Elyssa Winzeler < elyssa at linguistlist.org >
Subject: Multiword Expressions
 

	
-------------------------Message 1 ---------------------------------- 
Date: Mon, 27 Apr 2009 10:53:58
From: Elyssa Winzeler [elyssa at linguistlist.org]
Subject: Multiword Expressions

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Full Title: Multiword Expressions 
Short Title: MWE 09 

Date: 06-Aug-2009 - 06-Aug-2009
Location: Singapore, Singapore 
Contact Person: Dimitra Anastasiou
Meeting Email: dimitra at d-anastasiou.com
Web Site:
http://multiword.sourceforge.net/PHITE.php?sitesig=CONF&page=CONF_40_MWE_2009___lb__ACL__rb__


Linguistic Field(s): Applied Linguistics; Computational Linguistics; Discourse
Analysis; General Linguistics; Morphology; Semantics; Syntax; Text/Corpus
Linguistics; Translation 

Call Deadline: 01-Jun-2009 

Meeting Description:

Multi-Word Expressions (MWEs) are an indispensable part of natural languages and
appear steadily on a daily basis, both new and already existing but paraphrased.
Thus, the automated processing of MWEs is important for many natural language
applications. The meaning of MWEs can be either motivated or arbitrary. Native
speakers master most MWEs, while learners of a foreign language have to learn
MWEs by heart. The interpretation of MWEs poses a major challenge for automated
analysis helping both groups easily master MWEs.

The growing interest in MWEs in the NLP community has led to many specialized
workshops held every year since 2001 in conjunction with ACL, EACL and LREC;
there have been also two recent special issues on MWEs published by leading
journals: the International Journal of Language Resources and Evaluation, and
the Journal of Computer Speech and Language.

As a result of the overall progress in the field, the time has come to move from
basic preliminary research to actual applications in real-world NLP tasks.
Following this trend, the LREC-MWE'08 focused on gathering resources and
creating a common repository in order to rank MWE candidates and facilitate
further research. 

Call for Papers
Deadline Extended to June 1, 2009

In MWE'09 we are interested in the overall process of dealing with MWEs, asking
for original research related (but not limited) to the following four
fundamental topics.

(1) Identification.  Identification is a major problem for MWEs. The MWE
identification task is to determine whether a MWE is used non-compositionally
(figuratively) or compositionally (literally) in a particular context. The
identification of MWEs by automated means is a difficult task, as it does not
suffice to store the MWE into a dictionary database. Rule-based (morphosyntactic
rules) and/or statistical approaches may be needed to identify MWEs in context.

(2) Interpretation. Semantic interpretation of MWEs, particularly noun compounds
and determinerless prepositional phrases, is the task of determining the
implicit semantic relation holding between the MWE's sub-components. This
specific area is inviting research on (linguistically) identifying the semantic
relations (SRs) and automatic SR interpretation in MWEs. The relation
inventories used can be of different granularity and dependent on the particular
type of MWE construction. In some cases, MWE's semantics can be also specified
in terms of a suitable paraphrase.

(3) Disambiguation. Disambiguation (Semantic classification) is the task of
specifying the semantics of MWEs based on an inventory of semantic relations. It
tends to presuppose the ability to classify the (degree of) compositionality of
MWEs and applies only to compositional MWEs. The aim is to specify the semantics
of MWEs in terms of predefined semantic categories, e.g., in WordNet.

(4) Applications. Identifying MWEs in context and understanding their syntax and
semantics is important for many natural language applications, including but not
limited to question answering, machine translation, information retrieval,
information extraction, and textual entailment. Still, despite the growing
research interest, there are not enough successful applications in real NLP
problems, which we believe is the key for the advancement of the field.





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LINGUIST List: Vol-20-1609