Automatic Selection of Class Labels from a Thesaurus for an Effective Semantic Tagging of Corpora.

Automatic Selection of Class Labels from a Thesaurus for an Effective Semantic Tagging of Corpora. Alessandro Cucchiarelli t Paola Velardi ~1 JrUniversit~ di Ancona - alex@inform.unian.it :~Universit/I di Roma 'La Sapienza' - velardi@dsi.uniromal.it Abstract It is widely accepted that tagging text with se- mantic information would improve the quality of lexical learning in corpus-based NLP methods. However available on-line taxonomies are rather entangled and introduce an unnecessary level of ambiguity. The noise produced by the re- dundant number of tags often overrides the ad- vantage of semantic tagging. In this paper we propose an automatic method to select from WordNet a subset of domain-appropriate cate- gories that effectively reduce the overambiguity of WordNet, and help at identifying and cate- gorise relevant language patterns in a more com- pact way. The method is evaluated against a manually tagged corpus, SEMCOR. 1 Introduction It is well known that statistically-based approaches to lexical knowledge acquisition are faced with the problem of low counts. Many language patterns (from simple co- occurrences to more complex syntactic associations among words) occur very rarely, or are never encoun- tered, in the learning corpus. Since rare patterns are the majority, the quality and coverage of lexical learning may result severely affected. The obvious strategy to reduce this problem is to gener- alise word patterns according to some clustering tech- niques. In the literature, two generalisation strategies have been adopted: Distributional approaches: Several papers adopt distribu- tional techniques to identify clusters of words according to some defined measure of similarity. Among these, in (Grishman and Sterling, 1994) a method is proposed to cluster syntactic triples, while in (Pereira and Tishby 1992, 1993), (Dagan et al., 1994) pure bigrams are anal- ysed. The most intuitive evaluation of the effectiveness of dis- tributional approaches to the problem of word general- ization is presented in (Grishman and Sterling, 1994). In this paper it is argued that distributional (called also smoothing) techniques introduce a certain degree of addi- tional error, because co-occurrences may be erroneously conflated in a cluster, and some of the co-occurrences be- ing generalized are themselves incorrect. In general the effect is a higher recall at the price of a lower precision. Another drawback of these methods is that, since clusters have only a numeric description, they are often hard to evaluate on a linguistic ground. Semantic tagging: Another adopted solution is to gener- 380 alise the observed word patterns by grouping patterns in which words have the same semantic tag. Semantic tags are assigned from on-line thesaura like WordNet (Basili et al, 1996) (Resnik, 1995), Roget's categories (Yarowsky 1992) (Chen and Chen, 1996), the Japanese BGH (Utsuro et al, 1993), or assigned manually (Basili et al, 1992) 1. The obvious advantage of semantic tags is that words are clustered according to an intuitive principle (they belong to the same concept) rather than to some probabilistic measure. Semantic tagging has been proven useful for learning and categorising interesting relations among words, and for systematic lexical learning in sublan- guages, as …

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