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Neural Computing and Applications

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01-08-2013 | Original Article

Product named entity recognition for Chinese query questions based on a skip-chain CRF model

Authors: Zhifeng Hao, Hongfei Wang, Ruichu Cai, Wen Wen

Published in: Neural Computing and Applications | Issue 2/2013

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Abstract

As more and more commercial information can be obtained from the Internet, product named entity recognition plays an important role in market intelligence management. In this paper, a product named entity recognition method based on a skip-chain CRF model is proposed. This method considers not only the dependence between neighboring words but also the fact that product named entities are often connected by a connective. In this situation, the dependence between the words around the connective is more important than the dependence between neighboring words. This information improves the result of product named entity recognition as shown in the experiments. Experimental results on corpuses of mobile phone and digital camera demonstrate that the skip-chain CRF model works well and produces better results than the linear-chain CRF model.

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Title: Product named entity recognition for Chinese query questions based on a skip-chain CRF model
Authors: Zhifeng Hao
Hongfei Wang
Ruichu Cai
Wen Wen
Publication date: 01-08-2013
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0922-5

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