基于多特征Bi-LSTM-CRF的影评人名识别研究
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摘要
近年来电影行业蓬勃发展,相关的信息抽取和分析技术日益受到行业内的重视,其中对电影主创人物的分析尤为重要。而电影评论作为观影群体的主要反馈信息,具有重要的分析价值。如何从影评中自动抽取主创人名成为重要的基础工作。然而评论中观众对人物的称谓方式多样复杂,而且新电影的影评中往往存在大量人名未登录词,传统方法难以有效识别。针对影评的这些特点,该文提出一种基于多特征Bi-LSTM-CRF的影评人名识别方法。该方法通过利用外部人名语料和未标注影评提取字符级的特征,并采用Bi-LSTM-CRF模型进行人名字符序列标注。实验结果表明,该方法能够有效识别影评中的复杂称谓和人名未登录词,从而有效地抽取影评中的人名实体。
Person name in the movie reviews is featured by abbreviations and neologisms,which decreases the performances of classical models(e.g.CRF).To deal with this issue,this paper proposes a novel person name recognition method named Multi-Feature Bi-LSTM-CRF Model.This model extracts relevant character-level features by using external corpora and unlabeled reviews,then applies Bi-LSTM-CRF to identify the sequence of person names.The experimental results show that our model can effectively identify different forms of person names in the movie reviews.
Person name in the movie reviews is featured by abbreviations and neologisms,which decreases the performances of classical models(e.g.CRF).To deal with this issue,this paper proposes a novel person name recognition method named Multi-Feature Bi-LSTM-CRF Model.This model extracts relevant character-level features by using external corpora and unlabeled reviews,then applies Bi-LSTM-CRF to identify the sequence of person names.The experimental results show that our model can effectively identify different forms of person names in the movie reviews.
引文
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(1)http://movie.weibo.com
[2]刘鸿宇,等.评价对象抽取及其倾向性分析[J].中文信息学报,2010,24(01):84-88.
[3]李中国,刘颖.边界模板和局部统计相结合的中国人名识别[J].中文信息学报,2006,20(05):44-50.
[4]倪吉,等.基于可信度模型的中文人名识别研究[J].中文信息学报,2011,25(03):45-50.
[5]曹波,苏一丹,邓琦.基于最大熵模型的中国人名自动识别[J].计算机工程与应用,2009,45(04):227-228.
[6]张素香,高国洋,戚银城.基于条件随机场的中国人名识别方法[J].郑州大学学报(理学版),2009,41(02):40-43.
[7]Chiu J P C,Nichols E.Named entity recognition with bidirectional LSTM-CNNs[J].Transactions of the Association for Computational Linguistics,2016,vol.4:357-370.
[8]Habibi M,et al.Deep learning with word embeddings improves biomedical named entity recognition[J].Bioinformatics,2017,33(14):i37-48.
[9]Pham T H,Le-Hong P.End-to-end recurrent neural network models for Vietnamese named entity recognition:Word-level vs.Character-level[J].Computational Linguistics,2017:219-232.
[10]Dong C,et al.Character-based LSTM-CRF with radical-level features for Chinese named entity recognition[M]//Natural Language Understanding and Intelligent Applications:Springer International Publishing,2016:239-250.
[11]Zhiheng Huang,Wei Xu,Kai Yu.Bidirectional LSTM-CRF models for sequence tagging.arXiv preprint arXiv:1508.01991,2015.
[12]Levy O,Goldberg Y.Dependency-based word embeddings[C]//Proceedings of Meeting of the Association for Computational Linguistics,2010:302-308.
[13]Chen X,et al.Joint learning of character and word embeddings[C]//Proceedings of International Conference on Artificial Intelligence.AAAI Press.2015:1236-1242.
[14]Tang D,et al.Learning sentiment-specific word embedding for Twitter sentiment classification[C]//Proceedings of Meeting of the Association for Computational Linguistics,2014:1555-1565.
[15]Mikolov T,et al.Distributed representations of words and phrases and their compositionality[J].Advances in Neural Information Processing Systems,2013,26:3111-3119.
[16]Lafferty J D,Mccallum A,Pereira F C N.Conditional random fields:Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning.Morgan Kaufmann Publishers.Inc.2001:282-289.
[17]王祖兴,吕钊,顾君忠.基于混合方法的中文人名识别研究[J].计算机工程与应用,2015,51(08):211-217.
[18]Pennington J,Socher R,Manning C.Glove:Global vectors for word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2014:1532-1543.
[19]Kingma D P,Ba J L.Adam:a method for stochastic optimization[C]//Proceedings of the International Conference on Learning Representations.2015:1-15.
[20]Srivastava N,et al.Dropout:A simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[21]Dey R,Salemt F M.Gate-variants of gated recurrent unit(GRU)neural networks[C]//Proceedings of the International Midwest Symposium on Circuits and Systems.IEEE,2017:1597-1600.
(1)http://movie.weibo.com