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Neural Computing and Applications

Erschienen in:

01.08.2013 | Original Article

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

verfasst von: Zhifeng Hao, Hongfei Wang, Ruichu Cai, Wen Wen

Erschienen in: Neural Computing and Applications | Ausgabe 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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Zhao J, Liu F (2008) Product named entity recognition in Chinese text. Lang Resour Eval 42(2):197–217CrossRef

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

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)

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

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)

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

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

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

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

Titel: Product named entity recognition for Chinese query questions based on a skip-chain CRF model
verfasst von: Zhifeng Hao
Hongfei Wang
Ruichu Cai
Wen Wen
Publikationsdatum: 01.08.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0922-5

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