计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 319-323.doi: 10.11896/jsjkx.201100105
王晓涵, 谭陈琛, 相艳, 余正涛
WANG Xiao-han, TAN Chen-chen, XIANG Yan, YU Zheng-tao
摘要： 涉案微博的评价对象抽取是一个特定领域的任务,其评价对象词表达多样且含义与通用领域不同,仅依赖于通用领域的词嵌入无法很好地表征这些评价对象词。为此,提出了一种综合利用领域词嵌入和通用词嵌入的涉案微博评价对象抽取方法。首先对涉案微博文本进行预训练,得到具有涉案领域特征的嵌入层,其次将微博评论分别输入两个嵌入层,得到不同领域对评价对象的表征结果并进行拼接操作,然后通过卷积层抽取出与案件相关的特征,最后利用分类器对序列进行标记,以提取涉案微博评价对象。实验结果表明,所提方法的F1值在#重庆公交车坠江案#和#奔驰女司机维权案#的两个数据集上分别达到了72.36%和71.02%,较现有的基准模型有所提升,验证了不同领域词嵌入对涉案微博评价对象抽取的影响。
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| [1]ZHANG S Q,DU S D,ZHANG X B,et al.Social Rumor Detection Method Based on Multimodal Fusion[J].Computer Science,2021,48(5):117-123. [2]ZHUANG L,JING F,ZHU X Y.Movie review mining and sum-marization[C]//Proceedings of the 15th ACM International Conference on Information and Knowledge Management.Arlington,Virginia,USA,2006:43-50. [3]BLAIR-GOLDENSOHN S,HANNAN K,MCDONALD R, et al.Building a sentiment summarizer for local service reviews[C]//Proceedings of the 2008 WWW Workshop on NLP in the Information Explosion Era(NLPIX 2008).Beijing,China,2008:339-348. [4]SONG X L,WANG S G,LI H X.Research on automatic identification of product evaluation object oriented to specific domain[J].Journal of Chinese Information Processing,2010,24(1):89-93. [5]ZHANG M,ZHANG Y,VO D T.Neural networks for open domain targeted sentiment[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing(EMNLP 2015).2015:348-355. [6]PORIA S,CAMBRIA E,GELBUKH A.Aspect extraction for opinion mining with a deep con-volutional neural network[J].Knowledge Based Systems,2016,108(15):42-49. [7]MA X Z,HOVY E.End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.ACL 2016:1064-1074. [8]WANG W,PAN S J,DAHLMEIER D,et al.Coupled multi- layer attentions for co-extraction of aspect and opinion terms[C]//Proceedings of AAAI Conference on Artificial Intelligence(AAAI 2017).2017:3316-3322. [9]ZHANG P,CHEN T,CHEN C.Aspect extraction method for Chinese microblog based on deep learning[J].Computer Engineering and Design,2018(8):246-250. [10] CHEN S,LIU J,WANG Y,et al.Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:6515-6524. [11]LI K,CHEN C,QUAN X,et al.Conditional augmentation for aspect term extraction via masked sequence-to-sequence generation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:7056-7066. [12]WEI Z,HONG Y,ZOU B,et al.Don't eclipse your arts due to small discrepancies:Boundary repositioning with a pointer network for aspect extraction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:3678-3684. [13] ZHOU X,WAN X,XIAO J.Representation Learning for Aspect Category Detection in Online Reviews[C]//Proceedings of Twenty-ninth AAAI Conference on Artificial Intelligence.AAAI Press,2015:417-424. [14]YIN Y,WEI F,DONG L,et al.Unsupervised word and depen- dency path embeddings for aspect term extraction[C]//Procee-dings of the International Joint Conference on Artificial Intelligence(IJCAI 2016).2016:2979-2985. [15]HE R,LEE W S,NG H T,et al.An unsupervised neural attention model for aspect extraction[C]//Proceedings of Annual Meeting of the Association for Computational Linguistics(ACL 2017).2017:1008-1015. [16]LI X,LAM W.Deep multi-task learning for aspect term extraction with memory interaction[C]//Proceedings of Empirical Methods on Natural Language Processing(EMNLP 2017).2017:457-462. [17]SHU L,HU X,BING L.Lifelong learning crf for supervised aspect extraction[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 2:Short Papers).2017:148-154. [18]LUO H,LI T,LIU B,et al.Improving aspect term extraction with bidirectional dependency tree representation[J].IEEE ACM Transactions on Audio,Speech,and Language Processing,2019,27(7):1201-1212. [19]LI Y Z,LIU T W,LI Q G,et al.Character-based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction[C]//Proceedings of the 10th Asian Conference on Machine Learning(PMLR 2018).2018:518-533. [20]YAN H,DENG B,LI X,et al.TENER:Adapting Transformer Encoder for Named Entity Recognition[J].arXiv:1911.04474,2019. |
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