NDLI: Exploiting dictionaries in named entity extraction: combining semi-Markov extraction processes and data integration methods

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Exploiting dictionaries in named entity extraction: combining semi-Markov extraction processes and data integration methods

Content Provider	ACM Digital Library
Author	Sarawagi, Sunita Cohen, William W.
Abstract	We consider the problem of improving named entity recognition (NER) systems by using external dictionaries---more specifically, the problem of extending state-of-the-art NER systems by incorporating information about the similarity of extracted entities to entities in an external dictionary. This is difficult because most high-performance named entity recognition systems operate by sequentially classifying words as to whether or not they participate in an entity name; however, the most useful similarity measures score entire candidate names. To correct this mismatch we formalize a semi-Markov extraction process, which is based on sequentially classifying segments of several adjacent words, rather than single words. In addition to allowing a natural way of coupling high-performance NER methods and high-performance similarity functions, this formalism also allows the direct use of other useful entity-level features, and provides a more natural formulation of the NER problem than sequential word classification. Experiments in multiple domains show that the new model can substantially improve extraction performance over previous methods for using external dictionaries in NER.
Starting Page	89
Ending Page	98
Page Count	10
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014065
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learning Data integration Named entity recognition Information extraction Sequential learning
Content Type	Text
Resource Type	Article

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