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 not metonymy, search the knowledge base for a relevant analogy in order to discriminate metaphorical relations
 
* Problem
 
* Problem
 
# Detects any kind of non-literalness in language (metaphors, metonymies and others), and not only metaphors
 
# Detects any kind of non-literalness in language (metaphors, metonymies and others), and not only metaphors
 
# Fail to detect high conventionality of metaphor
 
# Fail to detect high conventionality of metaphor
 
First, literalness
 
is distinguished from non-literalness using as an indicator.
 
 
In the case that non-literalness is detected, the respective
 
phrase is tested for being a metonymic relation using
 
hand-coded patterns (such as CONTAINERfor-
 
CONTENT).
 
 
If the system fails to recognize
 
metonymy, it proceeds to search the knowledge
 
base for a relevant analogy in order to discriminate
 
metaphorical relations from anomalous ones.
 
 
E.g.,
 
the sentence in (7) would be represented in this
 
framework as (car,drink,gasoline), which does not
 
satisfy the preference (animal,drink,liquid), as car
 
is not a hyponym of animal.
 
 
met* then searches its knowledge base for a triple containing a hypernym
 
of both the actual argument and the desired argument
 
and finds (thing,use,energy source), which
 
represents the metaphorical interpretation.
 
  
 
=== Goatly (1997) ===
 
=== Goatly (1997) ===

Revision as of 14:54, 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, 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.