GEometrical Models of Natural Language Semantics
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Endorsed by:
Endorsed by:

March 31, 2009
Room MC.3
Megaron Athens International Conference Center, Athens, Greece.

NEWS: Patrick Pantel will be GEMS 2009 invited speaker: ''Lexical Semantics in the Prime Time: Applications to Web Search''.

The GEMS 2009 workshop on semantic space, will be held in conjunction with the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL-09), which will take place in Athens, Greece, March 30 - April 3, 2009. The workshop is endorsed by the ACL Special Interest Group on the Lexicon - SIGLEX. and by the ACL Special Interest Group on Computational Semantics - SIGSEM.

Distributional models and semantic spaces represent a core topic in contemporary computational linguistics for their impact on advanced tasks and on other knowledge fields (such as social science and the humanities). Previous workshops in this area (e.g. the workshop on 'Contextual Information in Semantic Space Models', 2007, and the ESSLLI workshop on 'Distributional Lexical Semantics', 2008) reflect a still growing interest in the area in the last years.

The goal of the GEMS workshop is to further stimulate research on semantic spaces and distributional methods for NLP, by adopting an interdisciplinary approach to allow a proper exchange of ideas, results and resources among often independent communities. In particular, the workshop will provide a common ground for a fruitful discussion among experts of distributional approaches, collocational corpus analysis and machine learning; researchers interested in the use of statistical models in NLPapplications (e.g. question answering, summarization and textual entailment) and in other fields of science; and experts in formal computational semantics.

The workshop aims at gathering contemporary contributions to large scale problems in meaning representation, acquisition and use, based on distributional and vector space models. The workshop will also explore the impact of such techniques on complex linguistic tasks, such as linguistic knowledge acquisition, semantic role labeling, textual entailment recognition, question answering, document understanding/summarization and ontology learning.