Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Extracting Meronyms for a Biology Knowledge Base Using Distant Supervision
Xiao Ling, Daniel S Weld
Jun 29, 2013 (modified: Jun 29, 2013)AKBC 2013 submissionreaders: everyone
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.
Enter your feedback below and we'll get back to you as soon as possible.