Entity Relation Extraction Based on Convolutional Neural Network and Keywords Strategy
WANG Linyu1, WANG Li1, ZHENG Tingyi1,2
1.College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600 2. Department of Electrical and Power Engineering, Shanxi Institute of Energy, Jinzhong 030600
Abstract The conventional relation extraction methods are time consuming, the error propagation in feature selection is likely to emerge, and the deep learning methods only depend on word embeddings to learn features. Aiming at these problems, a relation extraction method based on convolutional neural network and keywords strategy is proposed. Based on feature of the word embeddings, the keywords feature is acquired by the term proportion-inverse sentence proportion (TP-ISP) keywords extraction algorithm based on sentence. Thus, the category division is increased and the deficiency of the network to automatically learn features from sentence is remedied. In the network training process, the chunk-based max pooling strategy is adopted to reduce the information loss by the traditional max-over-time pooling strategy. The experiment demonstrates that the proposed method improves the results of entity relation extraction.
About author:: (WANG Linyu, born in 1990, master student. Her research interests include machine learning and natural language processing.) (WANG Li(Corresponding author), born in 1971, Ph.D., professor. Her research inte-rests include big data calculation, data mining and social computing.) (ZHENG Tingyi, born in 1987, Ph.D., assistant professor. Her research interests include artificial intelligence and data analysis.)
[1] RINK B, HARABAGIU S M. UTD: Classifying Semantic Relations by Combining Lexical and Semantic Resources // Proc of the 5th International Workshop on Semantic Evaluation. Stroudsburg, USA: Association for Computational Linguistics, 2010: 256-259. [2] ZELENKO D, AONE C, RICHARDELLA A. Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 2010, 3: 1083-1106. [3] CULOTTA A, SORENSEN J. Dependency Tree Kernels for Relation Extraction // Proc of the 42nd Annual Meeting on Association for Computational Linguistics. Stroudsburg, USA: Association for Computational Linguistics, 2004. DOI: 10.3115/218955.1219009. [4] LIU C Y, SUN W B, CHAO W H, et al. Convolution Neural Network for Relation Extraction // Proc of the 9th International Confe-rence on Advanced Data Mining and Applications. New York, USA: Springer-Verlag, 2013, II: 231-242. [5] ZENG D J, LIU K, LAI S W, et al. Relation Classification via Convolutional Deep Neural Network // Proc of the 25th International Conference on Computational Linguistics: Technical Papers. Stroudsburg, USA: Association for Computational Linguistics, 2014: 2335-2344. [6] NGUYEN T H, GRISHMAN R. Relation Extraction: Perspective from Convolutional Neural Networks // Proc of the 1st Workshop on Vector Space Modeling for Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2015: 39-48 [7] SANTOS C N D, XIANG B, ZHOU B W. Classifying Relations by Ranking with Convolutional Neural Networks // Proc of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Proce-ssing. Stroudsburg, USA: Association for Computational Linguistics, 2015: 626-634. [8] XU K, FENG Y S, HUANG S F, et al. Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2015: 536-540. [9] ZHENG S C, XU J M, ZHOU P, et al. A Neural Network Framework for Relation Extraction: Learning Entity Semantic and Relation Pattern. Knowledge-Based Systems, 2016, 114: 12-23. [10] JOHNSON R, ZHANG T. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks // Proc of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, USA: Association for Computational Linguistics, 2015: 103-112. [11] 吴嘉伟.电子病历实体关系抽取研究.硕士学位论文.哈尔滨:哈尔滨工业大学, 2014. (WU J W. Research on Relation Extraction in Electronic Medical Records. Master Dissertation. Harbin, China: Harbin Institute of Technology, 2014.) [12] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving Neural Networks by Preventing Co-adaptation of Feature Detectors. Computer Science, 2012, 3(4): 212-223. [13] KIM Y. Convolutional Neural Networks for Sentence Classification // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2014: 1746-1751 [14] VU N T, ADEL H, GUPTA P, et al. Combining Recurrent and Convolutional Neural Networks for Relation Classification // Proc of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, USA: Association for Computational Linguistics, 2016: 534-539. [15] SOCHER R, HUVAL B, MANNING C D, et al. Semantic Compositionality through Recursive Matrix-Vector Spaces // Proc of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg, USA: Association for Computational Linguistics, 2012: 1201-1211. [16] COLLOBERT R, WESTON J, BOTTOU L, et al. Natural Language Processing (almost) from Scratch. Journal of Machine Learning Research, 2011, 12: 2493-2537. [17] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed Representations of Words and Phrases and Their Compositionality // Proc of the 26th International Conference on Neural Information Proce-ssing Systems. Cambridge, USA: The MIT Press, 2013: 3111-3119. [18] 林古立,彭 宏,马千里,等.一种基于关键词的网页搜索结果多样化方法.华南理工大学学报(自然科学版), 2011, 39(5): 102-107. (LIN G L, PENG H, MA Q L, et al. A Keyword-Based Method for Diversification of Web Search Results. Journal of South China University of Technology(Natural Science Edition), 2011, 39(5):102-107.) [19] ZHANG J J, ZHANG D K, HAO J. Local Translation Prediction with Global Sentence Representation // Proc of the 24th International Conference on Artificial Intelligence. Buenos Aires, Argenti- na: AAAI Press, 2015: 1398-1404.
2017, Vol. 30 
