Publication

A Domain Adaptive Approach to Automatic Acquisition of Domain Relevant Terms and their Relations with Bootstrapping

Feiyu Xu, Daniela Kurz, Jakub Piskorski, Sven Schmeier

In: Proceedings of the 3rd International Conference on Language Resources an Evaluation (LREC'02), May 29-31. International Conference on Language Resources and Evaluation (LREC) 2002.

Abstract

In this paper, we present an unsupervised hybrid text-mining approach to automatic acquisition of domain relevant terms and their relations. We deploy the TFIDF-based term classification method to acquire domain relevant single-word terms. Further, we apply two strategies in order to learn lexico-syntatic patterns which indicate paradigmatic and domain relevant syntagmatic relations between the extracted terms. The first one uses an existing ontology as initial knowledge for learning lexico-syntactic patterns, while the second is based on different collocation acquisition methods to deal with the free-word order languages like German. This domain-adaptive method yields good results even when trained on relatively small training corpora. It can be applied to different real-world applications, which need domain-relevant ontology, for example, information extraction, information retrieval or text classification.

LREC_TermExtraction_final.pdf (pdf, 82 KB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz