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

• 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

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.

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.