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Extracting Meronyms for a Biology Knowledge Base Using Distant Supervision
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BibTeX
@MISC{Ling_extractingmeronyms,
author = {Xiao Ling and Peter Clark and Daniel S. Weld},
title = {Extracting Meronyms for a Biology Knowledge Base Using Distant Supervision},
year = {}
}
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Abstract
Knowledge of objects and their parts, meronym relations, are at the heart of many question-answering systems, but manually encoding these facts is impractical. Past researchers have tried hand-written patterns, supervised learning, and bootstrapped methods, but achieving both high precision and recall has proven elusive. This paper reports on a thorough exploration of distant supervision to learn a meronym extractor for the domain of college biology. We introduce a novel algorithm, generalizing the “at least one ” assumption of multi-instance learning to handle the case where a fixed (but unknown) percentage of bag members are positive examples. Detailed experiments compare strategies for mention detection, negative example generation, leveraging out-of-domain meronyms, and evaluate the benefit of our multi-instance percentage model.
Keyphrases
thorough exploration hand-written pattern out-of-domain meronym negative example generation past researcher meronym relation meronym extractor college biology bag member positive example novel algorithm multi-instance percentage model distant supervision multi-instance learning detailed experiment compare strategy mention detection high precision many question-answering system
