Using Textual Clues to Improve Metaphor Processing

Using textual clues to improve metaphor processing St6phane Ferrari LIMSI-CNRS PO Box 133 F-91403 Orsay cSdex, FRANCE ferrari@limsi.fr Abstract In this paper, we propose a textual clue ap- proach to help metaphor detection, in order to improve the semantic processing of this figure. The previous works in the domain studied the semantic regularities only, over- looking an obvious set of regularities. A corpus-based analysis shows the existence of surface regularities related to metaphors. These clues can be characterized by syn- tactic structures and lexical markers. We present an object oriented model for repre- senting the textual clues that were found. This representation is designed to help the choice of a semantic processing, in terms of possible non-literal meanings. A prototype implementing this model is currently un- der development, within an incremental ap- proach allowing step-by-step evaluations. 1 1 Introduction Metaphor is a frequently used figure of speech, re- flecting common cognitive processes. Most of the previous works in Natural Language Understanding (NLU) looked for regularities only on the semantic side of this figure, as shown in a brief overview in section 2. This resulted in complex semantic pro- cessings, not based on any previous robust detec- tion, or requiring large and exhaustive knowledge bases. Our aim is to provide NLU systems with a set of heuristics for choosing the most adequate seman- tic processing, as well as to give some probabilistic clues for disambiguating the possibly multiple mean- ing representations. A corpus-based analysis we made showed the exis- tence of textual clues in relation with the metaphors. These clues, mostly lexical markers combined with syntactic structures, are easy to spot, and can pro- vide a first set of detection heuristics. We propose, in 1This work takes part in a research project sponsored by the AUPELF-UREF (Francophone Agency For Edu- cation and Research) section 3, an object oriented model for representing these clues and their properties, in order to integrate them in a NLU system. For each class, attributes give information for spoting the clues, and, when possible, the source and the target of the metaphor, using the results of a syntactic parsing. A prototype, STK, partially implementing the model, is currently under development, within an incremental approach. It is Mready used to evaluate the clues relevance. In conclusion, we will discuss how the model can help chosing the adequate semantic analysis to pro- cess at the sentence level or disambiguating multiple meaning representations, providing probabilities for non-literal meanings. 2 Classical methods: a brief overview The classical NLU points of view of metaphor have pointed out the multiple kinds of relations between what is called the source and the target of the metaphor, but rarely discuss the problem of detect- ing the figure that bears the metaphor. For our pur- pose, we choose to present these approaches in two main groups, depending on how they initiate the se- mantic processing. The previous works led to a classification intro- duced by Dan Fass (Fass, 1991). In the compari- son view, the metaphor …

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