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BOpen information extraction using Wikipedia
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| Venue: | in Proc. 48th Annu. Meeting Assoc. Comput. Linguist., 2010 |
| Citations: | 96 - 3 self |
BibTeX
@INPROCEEDINGS{Wu_bopeninformation,
author = {Fei Wu and Daniel S. Weld},
title = {BOpen information extraction using Wikipedia},
booktitle = {in Proc. 48th Annu. Meeting Assoc. Comput. Linguist., 2010},
year = {},
pages = {118--127}
}
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Abstract
Information-extraction (IE) systems seek to distill semantic relations from naturallanguage text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the unbounded number of relations found on the Web. But how well can these open systems perform? This paper presents WOE, an open IE system which improves dramatically on TextRunner’s precision and recall. The key to WOE’s performance is a novel form of self-supervised learning for open extractors — using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data. Like TextRunner, WOE’s extractor eschews lexicalized features and handles an unbounded set of semantic relations. WOE can operate in two modes: when restricted to POS tag features, it runs as quickly as TextRunner, but when set to use dependency-parse features its precision and recall rise even higher. 1
Keyphrases
bopen information extraction open ie system semantic relation naturallanguage text textrunner precision extractor eschews novel form heuristic match unbounded set open extractor supervised learning relation-specific example training data wikipedia infobox attribute value po tag feature open system woe performance unbounded number self-supervised learning dependency-parse feature
