Computer Laboratory

Technical reports

Computational approaches to figurative language

Ekaterina V. Shutova

August 2011, 219 pages

This technical report is based on a dissertation submitted March 2011 by the author for the degree of Doctor of Philosophy to the University of Cambridge, Pembroke College.

Abstract

The use of figurative language is ubiquitous in natural language text and it is a serious bottleneck in automatic text understanding. A system capable of interpreting figurative language would be extremely beneficial to a wide range of practical NLP applications. The main focus of this thesis is on the phenomenon of metaphor. I adopt a statistical data-driven approach to its modelling, and create the first open-domain system for metaphor identification and interpretation in unrestricted text. In order to verify that similar methods can be applied to modelling other types of figurative language, I then extend this work to the task of interpretation of logical metonymy.

The metaphor interpretation system is capable of discovering literal meanings of metaphorical expressions in text. For the metaphors in the examples “All of this stirred an unfathomable excitement in her” or “a carelessly leaked report” the system produces interpretations “All of this provoked an unfathomable excitement in her” and “a carelessly disclosed report” respectively. It runs on unrestricted text and to my knowledge is the only existing robust metaphor paraphrasing system. It does not employ any hand-coded knowledge, but instead derives metaphorical interpretations from a large text corpus using statistical pattern-processing. The system was evaluated with the aid of human judges and it operates with the accuracy of 81%.

The metaphor identification system automatically traces the analogies involved in the production of a particular metaphorical expression in a minimally supervised way. The system generalises over the analogies by means of verb and noun clustering, i.e. identification of groups of similar concepts. This generalisation makes it capable of recognising previously unseen metaphorical expressions in text, e.g. having once seen a metaphor ‘stir excitement’ the system concludes that ‘swallow anger’ is also used metaphorically. The system identifies metaphorical expressions with a high precision of 79%.

The logical metonymy processing system produces a list of metonymic interpretations disambiguated with respect to their word sense. It then automatically organises them into a novel class-based model of logical metonymy inspired by both empirical evidence and linguistic theory. This model provides more accurate and generalised information about possible interpretations of metonymic phrases than previous approaches.

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BibTeX record

@TechReport{UCAM-CL-TR-803,
  author =	 {Shutova, Ekaterina V.},
  title = 	 {{Computational approaches to figurative language}},
  year = 	 2011,
  month = 	 aug,
  url = 	 {http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-803.pdf},
  institution =  {University of Cambridge, Computer Laboratory},
  number = 	 {UCAM-CL-TR-803}
}