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

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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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Notes

  1. Baidu knows: http://zhidao.baidu.com/.

References

  1. Zhao J, Liu F (2008) Product named entity recognition in Chinese text. Lang Resour Eval 42(2):197–217

    Article  Google Scholar 

  2. Pierre JM (2002) Mining knowledge from text collections using automatically generated metadata. In: Proceedings of fourth international conference on practical aspects of knowledge management

  3. Yu Hk, Zhang H, Liu Q, Lv X, Shi S (2006) Chinese named entity identification using cascaded hidden Markov model. J Commun 27(2):87–93 (in Chinese)

    Google Scholar 

  4. Bick E (2004) A named entity recognizer for Danish. In: Proceedings of 4th international conference on language resources and evaluation, pp 305–308

  5. Liu F, Zhao J, Lv B, Xu B, Yu H, Xia Y (2006) Study on product named entity recognition for business information extraction. J Chin Info Process 20(1):7–13 (in chinese)

    Google Scholar 

  6. Huang L, Liu Q (2008) Method for Chinese product name recognition based on conditional random fields. Appl Res Comput 25:1829–1831 (in Chinese)

    Google Scholar 

  7. Luo F, Xiao H, Chang W (2011) Product named entity recognition using conditional random fields. In: 2011 Fourth international conference on business intelligence and financial engineering

  8. Sutton C, McCallum A (2007) An introduction to conditional random fields for relational learning. In: Getoor L, Taskar B (eds) Introduction to statistical relational learning. MIT Press, Cambridge

    Google Scholar 

  9. Niu C, Li W, Ding Jh, Srihari RK (2003) A bootstrapping approach to named entity classification using successive learners. In: Proceedings of the 41st ACL, Sapporo, Japan, pp 335–342

  10. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the international conference on machine learning (ICML-2001)

  11. McCallum A, Wellner B (2005) Conditional models of identity uncertainty with application to noun coreference. In: Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems, vol 17. MIT Press, Cambridge, pp 905–912

  12. Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: Eighteenth conference on uncertainty in artificial intelligence (UAI02)

  13. McCallum AK (2002) MALLET: a machine learning for language toolkit. http://mallet.cs.umass.edu

  14. Zhang HP, Yu HK, Xiong DY, Liu Q (2003) HHMM-based Chinese lexical analyzer ICTCLAS. SIGHAN ‘03. In: Proceedings of the second SIGHAN Workshop affiliated with 41th ACL, vol 17, pp 184–187

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Authors and Affiliations

  1. Faulty of Applied Mathematics, Guangdong University of Technology, Guangzhou, 510006, PA, China

    Zhifeng Hao & Hongfei Wang

  2. Faulty of Compute Science, Guangdong University of Technology, Guangzhou, 510006, PA, China

    Zhifeng Hao, Ruichu Cai & Wen Wen

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  1. Zhifeng Hao
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  2. Hongfei Wang
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  3. Ruichu Cai
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  4. Wen Wen
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Correspondence to Hongfei Wang.

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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

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