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"Buenos dias", "buenas noches" -- this was the first words in a foreign language I heard in my life, as a three-year old boy growing up in developing post-war Western Germany, where the first gastarbeiters had arrived from Spain. Fascinated by the strange sounds, I tried to get to know some more languages, the only opportunity being TV courses of English and French -- there was no foreign language education for pre-teen school children in Germany yet in those days. Read more



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Conference Information



Full Title: 2nd Workshop on Metaphor in NLP

      
Short Title: Metaphor 2014
Location: Baltimore, MD, USA
Start Date: 26-Jun-2014 - 26-Jun-2014
Contact: Ekaterina Shutova
Meeting Email: click here to access email
Meeting URL: https://sites.google.com/site/workshoponmetaphorinnlp/
Meeting Description: Metaphor processing is a rapidly growing area in NLP. The ubiquity of
metaphor 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.

The work on metaphor in NLP and AI started in the 1980s, providing us with
a wealth of ideas on the structure and mechanisms of the phenomenon. The
last decade witnessed a technological leap in natural language computation,
whereby manually crafted rules gradually give 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 knowledge, these more recent
methods tend to have a wider coverage, as well as be more efficient,
accurate and robust. However, even the statistical metaphor processing
approaches so far often focused on a limited domain or a subset of
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, open many new
avenues for creation of large-scale robust tools for recognition and
interpretation of metaphor.

The main focus of the workshop will be on computational modeling of
metaphor using state-of-the-art NLP techniques.
Linguistic Subfield: Cognitive Science; Computational Linguistics
LL Issue: 25.1057


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