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Name entity recognition using language models

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1 Author(s)
Zhong-Hua Wang ; IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

The paper presents a new statistical name entity recognition algorithm, which does not require the collection and manual annotation of domain-specific sentences to train the models. The models of the name entities are domain-independent and could be directly applied to other domains of applications. This technique can also be applied to decode a set of raw sentences iteratively, if available, and use the decoded output to improve the statistical models. Applied to the mutual fund trading application, this new technique achieves a performance comparable to that using the decision tree model, which is trained from an annotated corpus. Iterative decoding of a set of natural language utterances and training of the general language model decreases the sentence error rate by 11%.

Published in:

Automatic Speech Recognition and Understanding, 2003. ASRU '03. 2003 IEEE Workshop on

Date of Conference:

30 Nov.-3 Dec. 2003