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2004
ACM

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

10 years 3 months ago
Exploiting dictionaries in named entity extraction: combining semi-Markov extraction processes and data integration methods
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 highperformance 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 sequentia...
William W. Cohen, Sunita Sarawagi
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2004
Where KDD
Authors William W. Cohen, Sunita Sarawagi
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