A Combination Training Framework for Domain-Specific Nominal Entity Recognition

In this paper, we present a combination training framework for building a domain-specific nominal entity recognition model. To reduce the huge cost in domain-specific corpus collection and tagging, this combination training framework leverages the existing nominal entity tagged corpus and nominal entity recognition model built in general domain. Meanwhile, a web-based automatic corpus construction mechanism is applied to collect the domain-specific data from the search results on the web. Experimental results show that this combination training framework can significantly reduce the training cost in building a domain-specific nominal entity recognition model. It provides an ease-to-use way for building a domain-specific nominal entity recognition model with less time and efforts.

By: Hong Lei Guo; Zhi Li Guo

Published in: RC23590 in 2005


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