Learning Relations Using Collocations

This paper describes the application of statistical analysis of large corpora to the problem of extracting semantic relations from unstructured text. We regard this approach as a viable method for generating input for the construction of ontologies as ontologies use well-defined semantic relations as building blocks (cf. van der Vet & Mars 1998). Starting from a short description of our corpora as well as our language analysis tools, we discuss in depth the automatic generation of collocation sets. We further give examples of different types of relations that may be found in collocation sets for arbitrary terms. The central question we deal with here is how to postprocess statistically generated collocation sets in order to extract named relations. We show that for different types of relations like cohyponyms or instance-of-relations, different extraction methods as well as additional sources of information can be applied to the basic collocation sets in order to verify the existence of a specific type of semantic relation for a given set of terms. 1 Analysis of Large Text Corpora Corpus Linguistics is generally understood as a branch of computational linguistics dealing with large text corpora for the purpose of statistical processing of language data (cf. Armstrong 1993, Manning & Schütze 1999). With the availability of large text corpora and the success of robust corpus processing in the nineties, this approach has recently become increasingly popular among computational linguists (cf. Sinclair 1991, Svartvik 1992). Since 1995 a German text corpus of more than 300 million words has been collected (cf. Quasthoff 1998B, Quasthoff & Wolff 2000), containing approx. 6 million different word forms in approx. 13 million sentences, which serves as input for the analysis methods described below. Similarly structured corpora have recently been set up for other European languages as well (English, French, Dutch), with more languages to follow in the near future (see table 1). German English Dutch French word tokens 300 M 250 M 22 M 15 M sentences 13.4 M 13 M 1.5 M 860,000 word types 6 M 1.2 M 600,000 230,000 Table 1: Basic Characteristics of the Corpora The basic goal of this corpus-based approach is to collect large amounts of textual data as input for semantic processing. Starting off from a rather simple data model tailored for large amounts of data and efficient processing using a relational data base system at storage level we employ a simple yet powerful technical infrastructure for processing texts to be included in the corpus. Beside basic procedures for text integration into the corpus various tools have been developed for post-processing linguistic data. Among them the automatic calculation of sentencebased word collocations stands out as an especially valuable tool for corpus-based language technology applications (see Quasthoff 1998A, Quasthoff & Wolff 2000). Additional, application oriented tools exist for search engine optimization as well as automatic document classification (see Heyer, Quasthoff & Wolff 2000). The corpora are available on the WWW (http://www. wortschatz. uni-leipzig.de) and may be used as a large online dictionary.