A Supervised Learning Approach to Acronym Identification

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ConferenceThe Eighteenth Canadian Conference on Artificial Intelligence (AI'2005), May 9-11, 2005.
AbstractThis paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features used by our supervised learning system, and our experiments with various learning schemes.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48121
NPARC number8913093
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Record identifier10d2325b-b18d-4ece-bbd3-9b1525b0443a
Record created2009-04-22
Record modified2016-05-09
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