XTRACT - AN OVERVIEW

Lexical collocations have particular statistical distributions. We hav e developed a set of statistical techniques for retrieving and identif ying collocations from large textual corpora. The techniques we develo ped are able to identify collocations of arbitrary length as well as f lexible collocations. These techniques have been implemented in a lexi cographic tool, Xtract, which is able to automatically acquire colloca tions with high retrieval performance. Xtract works in three stages. T he first stage is based on a statistical technique for identifying wor d pairs involved in a syntactic relation. The words can appear in the text in any order and can be separated by an arbitrary number of other words. The second stage is based on a technique to extract n-word col locations (or n-grams) in a much simpler way than related methods. The se collocations can involve closed class words such as particles and p repositions. A third stage is then applied to the output of stage one and applies parsing techniques to sentences involving a given word pai r in order to identify the proper syntactic relation between the two w ords. A secondary effect of the third stage is to filter out a number of candidate collocations as irrelevant and thus produce higher qualit y output. In this paper we present an overview of Xtract and we descri be several uses for Xtract and the knowledge it retrieves such as lang uage generation and machine translation.