L-ISA: Learning Domain Specific Isa-Relations from the Web

Abstract

Automated extraction of ontological knowledge from text corpora is a relevant task in Natural Language Processing. In this paper, we focus on the problem of finding hypernyms for relevant concepts in a specific domain (e.g. Optical Recording) in the context of a concrete and challenging application scenario (patent processing). To this end information available on the Web is exploited. The extraction method includes four mains steps. Firstly, the Google search engine is exploited to retrieve possible instances of isa-patterns reported in the literature. Then, the returned snippets are filtered on the basis of lexico-syntactic criteria (e.g. the candidate hypernym must be expressed as a noun phrase without complex modifiers). In a further filtering step, only candidate hypernyms compatible with the target domain are kept. Finally a candidate ranking mechanism is applied to select one hypernym as output of the algorithm. The extraction method was evaluated on 100 concepts of the Optical Recording domain. Moreover, the reliability of isa-patterns reported in the literature as predictors of isa-relations was assessed by manually evaluating the template instances remaining after lexico-syntactic filtering, for 3 concepts of the same domain. While more extensive testing is needed the method appears promising especially for its portability across different domains.

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