Using Textual Clues to Improve Metaphor Processing

In this paper, we propose a textual clue approach 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, overlooking an obvious set of regularities. A corpus-based analysis shows the existence of surface regularities related to metaphors. These clues can be characterized by syntactic structures and lexical markers. We present an object oriented model for representing 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 under development, within an incremental approach allowing step-by-step evaluations. 1 1 I n t r o d u c t i o n Metaphor is a frequently used figure of speech, reflecting 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 processings, not based on any previous robust detection, or requiring large and exhaustive knowledge bases. Our aim is to provide NLU systems with a set of heuristics for choosing the most adequate semantic processing, as well as to give some probabilistic clues for disambiguating the possibly multiple meaning representations. A corpus-based analysis we made showed the existence of textual clues in relation with the metaphors. These clues, mostly lexical markers combined with syntactic structures, are easy to spot, and can provide 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 Education 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 process at the sentence level or disambiguating multiple meaning representations, providing probabilities for non-literal meanings. 2 C l a s s i c a l m e t h o d s : a b r i e f