Relational learning via propositional algorithms: An information extraction case study

Dan Roth, Wen Tau Yih

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper develops a new paradigm for relational learning which allows for the representation and learning of relational information using propositional means. This paradigm suggests different tradeoffs than those in the traditional approach to this problem - the ILP approach - and as a result it enjoys several significant advantages over it. In particular, the new paradigm is more flexible and allows the use of any propositional algorithm, including probabilistic algorithms, within it. We evaluate the new approach on an important and relation-intensive task - Information Extraction - and show that it outperforms existing methods while being orders of magnitude more efficient.

Original languageEnglish (US)
Pages (from-to)1257-1263
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - Dec 1 2001
Event17th International Joint Conference on Artificial Intelligence, IJCAI 2001 - Seattle, WA, United States
Duration: Aug 4 2001Aug 10 2001

ASJC Scopus subject areas

  • Artificial Intelligence

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