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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Notes
Baidu knows: http://zhidao.baidu.com/.
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Hao, Z., Wang, H., Cai, R. et al. Product named entity recognition for Chinese query questions based on a skip-chain CRF model. Neural Comput & Applic 23, 371–379 (2013). https://doi.org/10.1007/s00521-012-0922-5
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DOI: https://doi.org/10.1007/s00521-012-0922-5