多场景文本的细粒度命名实体识别
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摘要
命名实体识别一直是数据挖掘领域的经典问题之一,尤其随着网络数据的剧增,如果能对多来源的文本数据进行多领域、细粒度的命名实体识别,显然能够为很多的数据挖掘应用提供支持。该文提出一种多领域、细粒度的命名实体识别方法,利用网络词典回标文本数据获得了大量的粗糙训练文本。为防止训练文本中的噪声干扰命名实体识别的结果,该算法将命名实体识别的过程划分为两个阶段,第一个阶段先获得命名实体的领域标签,之后利用命名实体的上下文确定命名实体的细粒度标签。实验结果显示,该文提出的方法使F_1值在全领域上平均值达到了80%左右。
Name entity recognition is a classical research issue in data mining community.To recognize the entities in multi-domain with fine-grained labels,we propose a method of utilizes web thesaurus to annotate web data automatically to acquire large-scale training corpus.To minimize the influence of the noises in training corpus,we design a two-phase entity recognition method.First,the entity's domain label is obtained.After that,the context of each recognized entity is used to determine the fine-grained label for one entity.Experimental results demonstrate that the proposed method can obtain high accuracy on entity recognition in multiple domains.
Name entity recognition is a classical research issue in data mining community.To recognize the entities in multi-domain with fine-grained labels,we propose a method of utilizes web thesaurus to annotate web data automatically to acquire large-scale training corpus.To minimize the influence of the noises in training corpus,we design a two-phase entity recognition method.First,the entity's domain label is obtained.After that,the context of each recognized entity is used to determine the fine-grained label for one entity.Experimental results demonstrate that the proposed method can obtain high accuracy on entity recognition in multiple domains.
引文
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[2]Borthwick A,Grishman R.A maximum entropy approach to named entity recognition[D].New York U-niversity,Graduate School of Arts and Science,1999.
[3]McCallum A,Freitag D,Pereira F C N.Maximum entropy Markov models for information extraction and segmentation[C]//Proceedings of the 17th ICML,2000:591-598.
[4]Lafferty J,Mc Callum A,Pereira F C N.Conditional random fields:Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th ICML,2001:282-289.
[5]Ratnaparkhi A.A maximum entropy model for partof-speech tagging[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,1996:133-142.
[6]Collobert R,Weston J,Bootul L.Natural language processing(almost)from Scratch[J].arXiv preprine arXiv:1103.0398.2011.
[7]Mikolov T,Sutskever I,Chen K,et al.Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems,2013:3111-3119.
[8]Gers F A,Schraudolph N N,Schmidhuber J.Learning precise timing with LSTM recurrent networks[J].Journal of Machine Learning Research,2002,3(Aug):115-143.
[9]Huang Z,Xu W,Yu K.Bidirectional LSTM-CRFmodels for sequence tagging[J].arXiv preprint arXiv:1508.01991,2015:1-10.
[10]Ando R K,Zhang T.A framework for learning predictive structures from multiple tasks and unlabeled data[J].The Journal of Machine Learning Research,2005(6):1817-1853.
[11]Jing H Y,Zhang T.Named entity recognition through classifier combination[C]//Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL-2003,2003:168-171.
[12]Kim Yoon.Convolutional neural networks for sentence classification[C]//Proceedings of the 2014Conference on Empirical Methods in Natural Language Processing(EMNLP),2014:1746-1751.
[13]Zhang Y,Wallace B.A sensitivity analysis of(and practitioners'guide to)convolutional neural networks for sentence classification[J].arXiv preprint arXiv:1510.03820,2015.
[14]M Boden.A guide to recurrent neural networks and back-propagation[R].SICS Technical Report J 2002:03,SICS.
[15]Hammerton J.Named entity recognition with Long short-term memory[C]//Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003-Volume 4.Association for Computational Linguistics,2003:172-175.
[16]Tang D Qin B,Feng X.Effective LSTMs for targetdependent sentiment classification[C]//Proceedings of the COLING 2016,2016:3298-3307.
[17]Yin Q,Zhang Y,Zhang W.Chinese zero pronoun resolution with deep memory network[C]//Proceedings of the 2017Conference on Empirical Methods in Natural Language Processing,2017:1309-1318.