Models of metaphor in NLP

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)

• Identify a set of linguistic cues indicate metaphor
  • metaphorically speaking, utterly, completely, so to speak and, surprisingly, literally.

Peters & Peters (2000)

• Detect figurative language in the WordNet
• Search for systematic polysemy, which allows to capture metonymic and metaphorical relations

CorMet System (Mason, 2004)

• The *first attempt for source-target domain mapping*

This is done by “finding systematic variations in domain-specific selectional preferences, which are inferred from large, dynamically mined Internet corpora”. For example, Mason collects texts from the LAB domain and the FINANCE domain, in both of which pour would be a characteristic verb. In the LAB domain pour has a strong selectional preference for objects of type liquid, whereas in the FINANCE domain it selects for money. From this Mason’s system infers the domain mapping FINANCE – LAB and the concept mapping money – liquid. He compares the output of his system against the Master Metaphor List (Lakoff et al., 1991) containing hand-crafted metaphorical mappings between concepts. Mason reports an accuracy of 77%, although it should be noted that as any evaluation that is done by hand it contains an element of subjectivity.

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.