Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction

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Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction (2003)

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by Mary Elaine Califf , Raymond J. Mooney , David Cohn

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BibTeX

@INPROCEEDINGS{Califf03bottom-uprelational,
    author = {Mary Elaine Califf and Raymond J. Mooney and David Cohn},
    title = {Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction},
    booktitle = {},
    year = {2003},
    pages = {328--334}
}

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Abstract

Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER is a bottom-up learning algorithm that incorporates techniques from several inductive logic programming systems. We have implemented the algorithm in a system that allows patterns to have constraints on the words, part-of-speech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains.

Keyphrases

information extraction bottom-up relational learning pattern matching rule present encouraging experimental result sample document natural-language document good application bottom-up learning algorithm shallow text processing semantic class significant domain-specific knowledge specified set pattern-match rule machine learning part-of-speech tag relevant item

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