semantics (the semantics of words) has become an important part of
Natural Language Processing due to its practical application in a
number of areas such as machine translation, web & enterprise
search, ontology learning etc.
This fact can be observed by
looking at the increasing interest in the field of learning of
lexical semantics e.g. the last Semantic Evaluation Workshop
(SemEval-2007) consisted of 18 tasks ranging from the
traditional Word Sense Disambiguation (WSD) task to the most
recent of Word Sense Induction (WSI),
web people search, metonymy resolution and others.
Given the wide variety of applications exploiting lexical semantics it
is significant to focus on methods and techniques, which can overcome
the "Knowledge Acquisition Bottleneck" and deal with the
cost-prohibitive, error-prone and labor-intensive processes of creating
hand-tagged training data.
The scope of this workshop is limited to topics that cover
unsupervised and minimally supervised methods relevant to learning of
lexical semantics. We particularly invite novel submissions which focus
on issues related to feature selection for specific tasks such as WSD
or WSI or multi-word items, parameter tunning and unsupervised
learning and evaluating hyponymy relations, graph-based methods for
learning lexical semantics, learning of bilingual lexicons. Other
relevant topics are welcome.
The call for papers provides a more
extensive list of topics.
Suresh Manandhar, Department of Computer Science, University of York, UK, firstname.lastname@example.org
Ioannis Klapaftis, Department of Computer Science, University of York, UK, email@example.com