document: 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 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 .
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