Difference between revisions of "Models of metaphor in NLP"

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* 3-Stage Approaches
 
* 3-Stage Approaches
 
# Detect selectional preference violation
 
# Detect selectional preference violation
# If find, tested for being a metonymic relation using hand-coded patterns
+
# If find violations, tested for being a metonymic relation using hand-coded patterns
 
# If not metonymy, search the knowledge base for a relevant analogy in order to discriminate metaphorical relations
 
# If not metonymy, search the knowledge base for a relevant analogy in order to discriminate metaphorical relations
 
* Problem
 
* Problem

Revision as of 15:15, 7 October 2012

Citation

E. Shutova. 2010. Models of Metaphor in NLP. In Proceedings of ACL 2010, Uppsala, Sweden.

Online version

ACL anthology

Introduction

This is a review paper of modeling metaphors in NLP. The author devised it into two main tasks: "metaphor recognition" and "metaphor interpretation".

Metaphor Recognition

Met* System (Fass, 1991)

  • First attempt to identify and interpret metaphorical expression
  • Using selectional preference and hand-coded knowledge base
  • 3-Stage Approaches
  1. Detect selectional preference violation
  2. If find violations, tested for being a metonymic relation using hand-coded patterns
  3. If not metonymy, search the knowledge base for a relevant analogy in order to discriminate metaphorical relations
  • Problem
  1. Detects any kind of non-literalness in language (metaphors, metonymies and others), and not only metaphors
  2. Fail to detect high conventionality of metaphor

Goatly (1997)

Peters & Peters (2000)

CorMet System (Mason, 2004)

TroFi System(Birke & Sarkar, 2006)

Gedigan et al. (2006)

Krishnakumaran & Zhu (2007)

Metaphor Interpretation

MIDAS System (Martin, 1990)

KARMA System (Narayanan, 1997), ATT-Meta (Barnden and Lee, 2002)

Veale and Hao (2008)

Shutova (2010)

Metaphor Resources

Metaphor Annotation in Corpora

Metaphor & Polysemy

Metaphor Identification

Pragglejaz Procedure

Source - Target Domain Vocabulary

Annotating Source and target Domains

Related papers

The widely cited Pang et al EMNLP 2002 paper was influenced by this paper - but considers supervised learning techniques. The choice of movie reviews as the domain was suggested by the (relatively) poor performance of Turney's method on movies.

An interesting follow-up paper is Turney and Littman, TOIS 2003 which focuses on evaluation of the technique of using PMI for predicting the semantic orientation of words.