Entity recognition of clothing commodity attributes

doi:10.11772/j.issn.1001-9081.2015.07.1945

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (7): 1945-1949.DOI: 10.11772/j.issn.1001-9081.2015.07.1945

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Entity recognition of clothing commodity attributes

ZHOU Xiang¹, LI Shaobo^1,2, YANG Guanci²

1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China;
2. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education (Guizhou University), Guiyang Guizhou 550003, China

Received:2015-02-04 Revised:2015-04-04 Online:2015-07-17 Published:2015-07-10

服装类商品属性实体识别

周详¹, 李少波^1,2, 杨观赐²

1. 中国科学院成都计算机应用研究所, 成都 610041;
2. 现代制造技术教育部重点实验室(贵州大学), 贵阳 550003

通讯作者: 周详(1989-),男,河南宜阳人,硕士研究生,主要研究方向:自然语言处理,codant@163.com
作者简介:李少波(1973-),男,湖南岳阳人,教授,博士生导师,博士,主要研究方向:计算智能、智能系统; 杨观赐(1983-),男,湖南嘉禾人,副教授,博士,主要研究方向:计算智能、智能系统。
基金资助:
国家科技支撑计划项目(2012BAF12B14);国家自然科学基金资助项目(51475097)。

Abstract

Abstract:

For the entity recognition of commodity attributes in clothing commodity title, a hybrid method combining Conditional Random Field (CRF) with entity boundary detecting rules was proposed. Firstly, the hidden entity hint character messages were obtained through a statistical method; secondly, statistical word indicators and their implications were interpreted with a granularity of character; thirdly, entity boundary detecting rules was proposed based on the entity hint characters and statistical word indicators; finally, a method for identifying threshold values in rules was proposed based on empirical risk minimization. In the comparison experiments with character-based CRF models, the overall precision, recall and F1 score were increased by 1.61%, 2.54% and 2.08% respectively, which validated the efficiency of the entity boundary detecting rule. The proposed method can be used in e-commerce Information Retrieval (IR), e-commerce Information Extraction (IE) and query intention identification, etc.

Key words: Named Entity Recognition (NER), clothing commodity, Conditional Random Field (CRF), e-commerce

摘要：

针对服装类商品标题中的商品属性实体识别问题,提出了一种边界探测规则与条件随机场(CRF)相结合的混合方法。首先,使用统计方法挖掘隐蔽的实体提示字信息;然后,以字为粒度对三种统计成词指标及其内涵进行了阐释;接着,基于统计成词指标和提示字信息设计了实体边界探测规则;最后,基于经验风险最小化给出了规则中阈值的确定方法。在与字标注的CRF模型的对比实验中,总体准确率、召回率、F1值分别提升了1.61%、2.54%和2.08%,验证了对于实体边界探测规则的有效性。所提方法可用于电子商务信息检索(IR)、电子商务信息抽取(IE)、查询意图识别等任务。

关键词: 命名实体识别, 服装类商品, 条件随机场, 电子商务

CLC Number:

TP391.1

ZHOU Xiang, LI Shaobo, YANG Guanci. Entity recognition of clothing commodity attributes[J]. Journal of Computer Applications, 2015, 35(7): 1945-1949.

周详, 李少波, 杨观赐. 服装类商品属性实体识别[J]. 计算机应用, 2015, 35(7): 1945-1949.

References

[1] LAFFERTY J, McCALLUM A, PEREIRA F. Conditional random fields: probabilistic models for segmenting and labeling sequence data [C]// ICML 2001: Proceedings of the 2001 International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers, 2001: 282-289.
[2] FENG Y, SUN L, ZHANG D, et al. Study on the Chinese named entity recognition using small scale tail hints [J]. Acta Electronica Sinaca, 2008, 36(9): 1833-1838.(冯元勇,孙乐,张大鲲,等.基于小规模尾字特征的中文命名实体识别研究[J].电子学报,2008,36(9):1833-1838.)
[3] XIANG X, SHI X, ZENG H. Chinese named entity recognition system using statistics-based and rules-based method [J]. Journal of Computer Applications, 2005, 25(10): 2404-2406.(向晓雯,史晓东,曾华琳.一个统计与规则相结合的中文命名实体识别系统[J].计算机应用,2005,25(10):2404-2406.)
[4] SHE J, ZHANG X. Musical named entity recognition method [J]. Journal of Computer Applications, 2010,30(11): 2928-2931.(佘俊,张学清.音乐命名实体识别方法[J].计算机应用,2010,30(11):2928-2931.)
[5] ZHANG J, WANG S, QIAN C. CRF and rules-based recognition of medical institutions name in Chinese [J]. Computer Applications and Software, 2014,31(3):159-162.(张金龙,王石,钱存发.基于CRF和规则的中文医疗机构名称识别[J].计算机应用与软件,2014,31(3):159-162.)
[6] LIU F, ZHAO J, LYU B, et al. Study on product named entity recognition for business information extraction [J]. Journal of Chinese Information Processing, 2006, 20(1): 7-13.(刘非凡,赵军,吕碧波,等.面向商务信息抽取的产品命名实体识别研究[J].中文信息学报,2006,20(1):7-13.)
[7] ZHANG C, GUO J, XIAN Y, et al. Named entity recognition of the products with English based on conditional random fields [J]. Computer Engineering and Science, 2010, 32(6): 115-117.(张朝胜,郭剑毅,线岩团,等.基于条件随机场的英文产品命名实体识别[J].计算机工程与科学,2010,32(6):115-117.)
[8] MEI F. Research on product named entity recognition and normali-zation [D]. Harbin: Harbin Institute of Technology, 2011.(梅丰.产品名实体识别及规范化研究[D].哈尔滨:哈尔滨工业大学,2011.)
[9] LUO F, XIONG Q, XIAO M. Product named entity recognition based on ontology [J]. Journal of Wuhan University of Technology: Information and Management Engineering, 2011, 33(6): 948-952.(罗芳,熊前兴,肖敏.基于本体的产品命名实体识别研究[J].武汉理工大学学报:信息与管理工程版,2011,33(6):948-952.)
[10] LI H. Statistical learning methods [M]. Beijing: Tsinghua Univer-sity Press, 2012: 195-197.(李航.统计学习方法[M].北京:清华大学出版社,2012:195-197.)
[11] CHEN F, LIU Y, WEI C, et al. Open domain new word detection using conditional random field method [J]. Journal of Software, 2013, 24(5): 1051-1060.(陈飞,刘奕群,魏超,等.基于条件随机场方法的开放领域新词发现[J].软件学报,2013,24(5):1051-1060.)
[12] ZHAO X, ZHANG H. New words identification based on iterative algorithm [J]. Computer Engineering, 2014, 40(7): 154-158.(赵小宝,张华平.基于迭代算法的新词识别[J].计算机工程,2014,40(7):154-158.)
[13] FENG Y, SUN L, ZHANG D, et al. A rapid algorithm to Chinese named entity recognition based on single character hints [J]. Journal of Chinese Information Processing, 2008, 22(1): 104-110.(冯元勇,孙乐,李文波,等.基于单字提示特征的中文命名实体识别快速算法[J].中文信息学报,2008,22(1):104-110.)
[14] KUDO T. CRF++: yet another CRF toolkit [EB/OL]. [2014-12-01]. http://crfpp.googlecode.com/svn/trunk/doc/index.html.
[15] LIU D, NOCEDAL J. On the limited memory BFGS method for large scale optimization [EB/OL]. [2014-12-07]. http://users.iems.northwestern.edu/~nocedal/PDFfiles/limited-memory.pdf.

Entity recognition of clothing commodity attributes

服装类商品属性实体识别

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