[Elsnet-list] EACL 2014 Tutorial on Computational modelling of metaphor

Ekaterina Shutova katia at icsi.berkeley.edu
Thu Mar 6 12:26:15 CET 2014


CALL FOR PARTICIPATION

EACL 2014 Tutorial on Computational modelling of metaphor

Gothenburg, Sweden, 26 April

http://eacl2014.org/tutorial-metaphor

Instructors: Ekaterina Shutova and Tony Veale

TUTORIAL DESCRIPTION

Metaphor processing is a rapidly growing area in NLP.  Characteristic to
all areas of human activity (from the ordinary to the poetic or the
scientific) and, thus, to all types of discourse, metaphor poses an
important problem for NLP systems. Its ubiquity in language has been
established in a number of corpus studies and the role it plays in human
reasoning has been confirmed in psychological experiments. This makes
metaphor an important research area for computational and cognitive
linguistics, and its automatic identification and interpretation
indispensable for any semantics-oriented NLP application.

Computational work on metaphor in NLP and AI ignited in the 1970s and
gained momentum in the 1980s, providing a wealth of ideas on the form,
structure and mechanisms of the phenomenon. The last decade has witnessed a
technological leap in natural language computation, as manually crafted
rules have gradually given way to more robust corpus-based statistical
methods. This is also the case for metaphor research. In the recent years,
the problem of metaphor modeling has been steadily gaining interest within
the NLP community, with a growing number of approaches exploiting
statistical techniques. Compared to more traditional approaches based on
hand-coded resources, these more recent methods boast of a wider coverage,
as well as greater efficiency and robustness. However, even the statistical
metaphor processing approaches largely focus on a limited domain or a
subset of conceptual phenomena. At the same time, recent work on
computational lexical semantics and lexical acquisition techniques, as well
as a wide range of NLP methods applying machine learning to open-domain
semantic tasks, opens many new avenues for creation of large-scale robust
tools for the recognition and interpretation of metaphor.

Despite a growing recognition of the importance of metaphor to the semantic
and affective processing of language, and despite the availability of new
NLP tools that enable us to take metaphor processing to the next level,
educational initiatives for introducing the NLP community to this
fascinating area of research have been relatively few in number. Our
proposed tutorial thus addresses this gap, by aiming to:


   -

   introduce a CL audience to the main linguistic, conceptual and cognitive
   properties of metaphor;
   -

   cover the history of metaphor modelling and the state-of-the-art
   approaches to metaphor identification and interpretation
   -

   analyse the trends in computational metaphor research and compare
   different types of approaches, aiming to identify the most promising system
   features and techniques in metaphor modelling
   -

   discuss potential applications of metaphor processing in wider NLP
   -

   relate the problem of metaphor modelling to that of other types of
   figurative language


The tutorial is targeted both at participants who are new to the field and
need a comprehensive overview of metaphor processing techniques and
applications, as well as at experienced scientists who want to stay up to
date on the recent advances in metaphor research.


TUTORIAL OUTLINE

Introduction: Linguistic, cognitive and cultural properties of metaphor

   1.

   Linguistic metaphor
   2.

   Conceptual metaphor
   3.

   Metaphorical inference
   4.

   Extended metaphor / metaphor in discourse
   5.

   Conventional and novel metaphor
   6.

   Metaphor in corpora and lexical resources


Computational approaches to metaphor identification

   1.

   Knowledge-based methods
   2.

   Lexical resource-based methods
   3.

   Metaphor and selectional preferences
   4.

   Metaphor and abstractness
   5.

   Metaphor and cultural stereotypes
   6.

   Word similarity and association-based methods
   7.

   Supervised learning for metaphor identification
   8.

   Weakly-supervised and unsupervised methods


Computational approaches to metaphor interpretation

   1.

   Knowledge-based methods
   2.

   Metaphor interpretation by explanation (SlipNet)
   3.

   Metaphor interpretation as paraphrasing (supervised and unsupervised)
   4.

   Challenges in metaphor generation


Applications of metaphor processing systems

   1.

   Metaphor in machine translation
   2.

   Metaphor in opinion mining
   3.

   Metaphor in information retrieval
   4.

   Metaphor in educational applications
   5.

   Metaphor in social science
   6.

   Metaphor in psychology


Metaphor and other types of figurative language

   1.

   Metaphor and blending
   2.

   Metaphor and simile
   3.

   Metaphor and analogy
   4.

   Metaphor and irony




We look forward to seeing you at the tutorial!

Katia and Tony
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