Paper: URES : An Unsupervised Web Relation Extraction System

ACL ID P06-2086
Title URES : An Unsupervised Web Relation Extraction System
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006

Most information extraction systems ei- ther use hand written extraction patterns or use a machine learning algorithm that is trained on a manually annotated cor- pus. Both of these approaches require massive human effort and hence prevent information extraction from becoming more widely applicable. In this paper we present URES (Unsupervised Relation Extraction System), which extracts rela- tions from the Web in a totally unsuper- vised way. It takes as input the descriptions of the target relations, which include the names of the predicates, the types of their attributes, and several seed instances of the relations. Then the sys- tem downloads from the Web a large col- lection of pages that are likely to contain instances of the target relations. From those pages, utilizing the known see...