25.268, Calls: Cognitive Science, Computational Linguistics/USA

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LINGUIST List: Vol-25-268. Wed Jan 15 2014. ISSN: 1069 - 4875.

Subject: 25.268, Calls: Cognitive Science, Computational Linguistics/USA

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Anja Wanner, U of Wisconsin Madison
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Date: Wed, 15 Jan 2014 13:39:38
From: Ekaterina Shutova [shutova.e at gmail.com]
Subject: 2nd Workshop on Metaphor in NLP

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Full Title: 2nd Workshop on Metaphor in NLP 
Short Title: Metaphor 2014 

Date: 26-Jun-2014 - 26-Jun-2014
Location: Baltimore, MD, USA 
Contact Person: Ekaterina Shutova
Meeting Email: katia at icsi.berkeley.edu
Web Site: https://sites.google.com/site/workshoponmetaphorinnlp/ 

Linguistic Field(s): Cognitive Science; Computational Linguistics 

Call Deadline: 25-Mar-2014 

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.

2nd Call for Papers:

The Second Workshop on Metaphor in NLP 
(co-located with ACL 2014)
Baltimore, MD, USA – June 26, 2014
https://sites.google.com/site/workshoponmetaphorinnlp/
Submission deadline: March 25, 2014

The main focus of the workshop will be on computational modeling of
metaphor using state-of-the-art NLP techniques. However, papers on
cognitive, linguistic, and applied aspects of metaphor are also of
interest, provided that they are presented within a computational, a formal
or a quantitative framework. We also encourage descriptions of proposals
and data sets for shared tasks on metaphor processing. In comparison to
last year's workshop, the Second Workshop on Metaphor in NLP will broaden
its scope by encouraging submissions on special themes of computational
processing of emotions and affect in metaphor, as well as processing of
metaphorical language in social media.

The workshop will solicit both full papers and short papers for either oral
or poster presentation. 

Topics will include, but will not be limited to, the following:

Identification and interpretation of different levels and types of metaphor:

- Conceptual and linguistic metaphor
- Lexical metaphor
- Multiword metaphorical expressions
- Extended metaphor / metaphor in discourse
- Conventional / novel / deliberate metaphor

Metaphor processing systems that incorporate state-of-the-art NLP methods:

- Statistical metaphor processing
- The use of lexical resources for metaphor processing
- The use of corpora for metaphor processing
- Distributional methods for metaphor processing
- Supervised and unsupervised learning for metaphor processing
- Identification of conceptual and linguistic metaphor
- Identification and interpretation of lexical metaphor / multiword
metaphor / extended metaphor
- Lexical metaphor interpretation vs. word sense disambiguation
- Metaphor paraphrasing
- Generation of metaphorical expressions
- Metaphor translation and multilingual metaphor processing

Metaphor resources and evaluation:

- Metaphor annotation in corpora
- Metaphor in lexical resources
- Reliability of metaphor annotation
- Datasets for evaluation of metaphor processing tools
- Metaphor evaluation methodologies and frameworks
- Descriptions of proposals for shared tasks on metaphor processing

Metaphor processing for external NLP applications:

- Metaphor in machine translation 
- Metaphor in opinion mining
- Metaphor in information retrieval
- Metaphor in educational applications
- Metaphor in dialog systems
- Metaphor in open-domain and domain-specific applications

Metaphor and cognition:

- Computational approaches to metaphor inspired by cognitive evidence 
- Cognitive models of metaphor processing by the human brain
- Models of metaphor across languages and cultures

Metaphor interaction with other phenomena (within a computational, formal
or quantitative framework):

- Metaphor and compositionality
- Metaphor and abstractness / concreteness
- Metaphor and sentiment
- Metaphor and persuasion
- Metaphor and argumentation
- Metaphor and metonymy
- Metaphor and grammar

Metaphor and sentiment:

- The use of metaphorical language to express stronger sentiment / evaluation
- Sentiment processing systems that make use of metaphor as a feature
- Sentiment processing systems that detect affect associated with
metaphorical expressions







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