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Title An Algorithm that Learns What's in a Name
Edition Volume 34, Numbers 1-3
Call Number
ISBN/ISSN 0885-6125
Author(s) BIKEL,DANIEL M.
SCHWARTZ,RICHARD
WEISCHEDEL,RALPH M.
Subject(s)
Classification
Series Title Machine Learning
GMD Electronic Journal
Language English
Publisher Springer Netherlands
Publishing Year 1999
Publishing Place Netherlands
Collation 21p
Abstract/Notes In this paper, we present IdentiFinderTM, a hidden Markov model that learns to recognize and classify
names, dates, times, and numerical quantities. We have evaluated the model in English (based on data from the
Sixth and Seventh Message Understanding Conferences [MUC-6, MUC-7] and broadcast news) and in Spanish
(based on data distributed through the First Multilingual Entity Task [MET-1]), and on speech input (based on
broadcast news). We report results here on standard materials only to quantify performance on data available to the
community, namely, MUC-6 and MET-1. Results have been consistently better than reported by any other learning
algorithm. IdentiFinder’s performance is competitive with approaches based on handcrafted rules on mixed case
text and superior on text where case information is not available. We also present a controlled experiment showing
the effect of training set size on performance, demonstrating that as little as 100,000 words of training data
is adequate to get performance around 90% on newswire. Although we present our understanding of why this
algorithm performs so well on this class of problems, we believe that significant improvement in performance
may still be possible.
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