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
• A corpus-based approach to find systematic variations in domain-specific selectional preferences
• Take Master Metaphor List (Lakoff et al., 1991) as baseline, and achieve an accuracy

of 77% (judged by human)

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.