REN Chaogan1,2, YANG Yan1,2, JIA Zhen1,2, TANG Huijia1, YU Xiuying1
1.School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756 2.Key Laboratory of Cloud Computing and Intelligent Technology of Sichuan Province, Southwest Jiaotong University, Chengdu 611756
Abstract In question entity linking, a large amount of work in data processing and feature selection is required, cumulative errors are caused easily and the linking effect is reduced. To address the issues, an attention mechanism based encoder-decoder model for entity linking(AMEDEL) is proposed. In this model, long short-term memory network is utilized to encode the questions. Then, entity mentions and disambiguation information are generated as outputs through the decoder process by attention mechanism. Finally, these outputs are linked to the entities in knowledge base. The experiments are conducted on a dataset of questions and entities about products in automotive field. The results show that the proposed model obtains satisfactory results by only employing rare contextual information.
Fund:Supported by National Natural Science Foundation of China(No.61572407), National Key Technology Research and Development Program (2015BAH19F02)
About author:: (REN Chaogan, master student. His research interests include natural language processing and deep learning.) (YANG Yan(Corresponding author), Ph.D., professor. Her research interests include artificial intelligence, big data analysis and mi-ning and ensemble learning.) (JIA Zhen, Ph.D., lecturer. Her research interests include knowledge graph and natural language processing.) (TANG Huijia, master, professor. Her research interests include cloud service system and technology.) (YU Xiuying, Ph.D., lecturer. Her resear-ch interests include information security.)
[1] NADEAU D, SEKINE S. A Survey of Named Entity Recognition and Classification. Lingvisticae Investigationes, 2007, 30(1): 3-26. [2] BIKEL D M, SCHWARTZ R, WEISCHEDEL R M. An Algorithm That Learns What's in a Name. Machine Learning, 1999, 34(1/2/3): 211-231. [3] FRESKO M, ROSENFELD B, FELDMAN R. A Hybrid Approach to NER by MEMM and Manual Rules // Proc of the 14th ACM CIKM International Conference on Information and Knowledge Management. New York, USA: ACM, 2005: 361-362. [4] MCCALLUM A, LI W. Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons // Proc of the 7th Conference on Natural Language Learning at HLT-NAACL. Stroudsburg, USA: ACL, 2003, IV: 188-191. [5] ISOZAKI H, KAZAWA H. Efficient Support Vector Classifiers for Named Entity Recognition // Proc of the 19th International Confe-rence on Computational linguistics. Stroudsburg, USA: ACL, 2002, I: 1-7. [6] CARRERAS X, MÀRQUEZ L, PADRÓ L. Named Entity Extraction Using Adaboost // Proc of the 6th Conference on Natural Language Learning. Stroudsburg, USA: ACL, 2002: 167-170. [7] LAMPLE G, BALLESTEROS M, SUBRAMANIAN S, et al. Neural Architectures for Named Entity Recognition[C/OL]. [2018-04-20]. https://arxiv.org/pdf/1603.01360.pdf. [8] XU J, LU Q, LIU J, et al. NLPComp in TAC 2012 Entity Linking and Slot-Filling[C/OL]. [2018-04-20]. https://tac.nist.gov/publications/2012/participant.papers/NLPComp.proceedings.pdf. [9] ZHANG Y Y, DAI H J, KOZAREVA Z, et al. Variational Reaso-ning for Question Answering with Knowledge Graph[C/OL]. [2018-04-20]. https://arxiv.org/pdf/1709.04071.pdf. [10] 怀宝兴,宝腾飞,祝恒书,等.一种基于概率主题模型的命名实体链接方法.软件学报, 2014, 25(9): 2076-2087. (HUAI B X, BAO T F, ZHU H S, et al. Topic Modeling Approach to Named Entity Linking. Journal of Software, 2014, 25(9): 2076-2087.) [11] HAN X P, SUN L, ZHAO J. Collective Entity Linking in Web Text: A Graph-Based Method // Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2011: 765-774. [12] BAGGA A, BALDWIN B. Entity-Based Cross-Document Corefe-rencing Using the Vector Space Model // Proc of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics. Stroudsburg, USA: ACL, 1998, I: 79-85. [13] FRANCIS-LANDAU M, DURRETT G, KLEIN D. Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks // Proc of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, USA: ACL, 2016: 1256-1261. [14] 齐爱芹,徐蔚然.基于词向量的实体链接方法.数据采集与处理, 2017, 32(3): 604-611. (QI A Q, XU W R. Method of Entity Linking Based on Word Embedding. Journal of Data Acquisition and Processing, 2017, 32(3): 604-611.) [15] 毛二松,王 波,唐永旺,等.基于词向量的中文微博实体链接方法.计算机应用与软件, 2017, 34(4): 11-15. (MAO E S, WANG B, TANG Y W, et al. Entity Linking Method of Chinese Micro-blog Based on Word Vector. Computer Applications and Software, 2017, 34(4): 11-15.) [16] 冯 冲,石 戈,郭宇航,等.基于词向量语义分类的微博实体链接方法.自动化学报, 2016, 42(6): 915-922. (FENG C, SHI G, GUO Y H, et al. An Entity Linking Method for Microblog Based on Semantic Categorization by Word Embe-ddings. Acta Automatica Sinica, 2016, 42(6): 915-922.) [17] BAHDANAU D, CHO K, BENGIO Y. Neural Machine Translation by Jointly Learning to Align and Translate[C/OL]. [2018-04-20]. https://arxiv.org/pdf/1409.0473.pdf. [18] LUONG M T, PHAM H, MANNING C D. Effective Approaches to Attention-Based Neural Machine Translation // Proc of the Confe-rence on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2015: 1412-1421. [19] LIN Z H, FENG M W, DOS SANTOS C N, et al. A Structured Self-attentive Sentence Embedding[C/OL]. [2018-04-20]. https://arxiv.org/pdf/1703.03130.pdf. [20] DANILUK M, ROCKTÄSCHEL T, WELBL J, et al. Frustratingly Short Attention Spans in Neural Language Modeling[C/OL]. [2018-04-20]. https://arxiv.org/pdf/1702.04521.pdf. [21] KADLEC R, SCHMID M, BAJGAR O, et al. Text Understanding with the Attention Sum Reader Network // Proc of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2016, I: 908-918 [22] PARIKH A, TÄCKSTRÖM O, DAS D, et al. A Decomposable Attention Model for Natural Language Inference // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2016: 2249-2255. [23] PAULUS R, XIONG C, SOCHER R. A Deep Reinforced Model for Abstractive Summarization[C/OL]. [2018-04-20]. https://arxiv.org/pdf/1705.04304.pdf. [24] SUTSKEVER I, VINYALS O, LE Q V. Sequence to Sequence Learning with Neural Networks // GHAHRAMANI Z, WELLING M, CORTES C, et al., eds. Advances in Neural Information Processing Systems 27. Cambridge, USA: The MIT Press, 2014: 3104-3112.
2018, Vol. 31 
