|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
RULE-BASED NAMED ENTITY RECOGNITION SYSTEM FOR SPEECH INPUT
Ji-Hwan Kim and P.C. Woodland
In this paper, we propose a rule based (transformation based) named entity recognition system which uses the Brill rule inference approach. To measure its performance, we compare the performance of the rule-based system and IdentiFinder, one of the most successful stochastic systems. In the baseline case (no punctuation and no capitalisation), both systems show almost equal performance. They also have similar performance in the case of additional information such as punctuation, capitalisation and name lists. The performance of both systems degrade linearly with added speech recognition errors, and their rates of degradation are almost equal. These results show that automatic rule inference is a viable alternative to the HMM-based approach to named entity recognition, but it retains the advantages of a rule-based approach.
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