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 16:15, 7 October 2012
Contents
Citation
E. Shutova. 2010. Models of Metaphor in NLP. In Proceedings of ACL 2010, Uppsala, Sweden.
Online version
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
- Detect selectional preference violation
- 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
- Problem
- Detects any kind of non-literalness in language (metaphors, metonymies and others), and not only metaphors
- 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.
