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Abstract: entity linking (EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of the global model, but ignore latent semantic information in the local model and the acquisition of effective entity type information. In this paper, we propose two adaptive features, in which the first adaptive feature enables the local and global models to capture latent information, and the second adaptive feature describes effective information for entity type embeddings. These adaptive features can work together naturally to handle some uncertain entity type information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets, and the best average performance on out-domain datasets. These results indicate that the proposed adaptive features, which are based on their own diverse contexts, can capture information that is conducive for EL.

利用两个自适应特征改进实体链接

[1]Bunescu R, Paşca M, 2006. Using encyclopedic knowledge for named entity disambiguation. Proc 11^th Conf of the European Chapter of the Association for Computational Linguistics, p.9-16.

[2]Cao YX, Hou L, Li JZ, et al., 2018. Neural collective entity linking. Proc 27^th Int Conf on Computational Linguistics, p.675-686.

[3]Chen S, Wang JP, Jiang F, et al., 2020. Improving entity linking by modeling latent entity type information. Proc 34^th AAAI Conf on Artificial Intelligence, p.7529-7537.

[4]Deng ZH, Li ZX, Yang Q, et al., 2020. Improving entity linking with graph networks. Proc 21^st Int Conf on Web Information Systems Engineering, p.343-354.

[5]Devlin J, Chang MW, Lee K, et al., 2019. BERT: pre-training of deep bidirectional transformers for language understanding. Proc Conf of the North American Chapter of the Association for Computational Linguistics, p.4171-4186.

[6]Durrett G, Klein D, 2014. A joint model for entity analysis: coreference, typing, and linking. Trans Assoc Comput Linguist, 2:477-490.

[7]Fang W, Zhang JW, Wang DL, et al., 2016. Entity disambiguation by knowledge and text jointly embedding. Proc 20^th SIGNLL Conf on Computational Natural Language Learning, p.260-269.

[8]Fang Z, Cao YN, Li Q, et al., 2019. Joint entity linking with deep reinforcement learning. Proc World Wide Web Conf, p.438-447.

[9]Francis-Landau M, Durrett G, Klein D, 2016. Capturing semantic similarity for entity linking with convolutional neural networks. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.1256-1261.

[10]Ganea OE, Hofmann T, 2017. Deep joint entity disambiguation with local neural attention. Proc Conf on Empirical Methods in Natural Language Processing, p.2619-2629.

[11]Globerson A, Lazic N, Chakrabarti S, et al., 2016. Collective entity resolution with multi-focal attention. Proc 54^th Annual Meeting of the Association for Computational Linguistics, p.621-631.

[12]Guo ZC, Barbosa D, 2018. Robust named entity disambiguation with random walks. Semant Web, 9(4):459-479.

[13]Han XP, Sun L, 2011. A generative entity-mention model for linking entities with knowledge base. Proc 49^th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, p.945-954.

[14]Hoffart J, Yosef MA, Bordino I, et al., 2011. Robust disambiguation of named entities in text. Proc Conf on Empirical Methods in Natural Language Processing, p.782-792.

[15]Hoffmann R, Zhang CL, Ling X, et al., 2011. Knowledge-based weak supervision for information extraction of overlapping relations. Proc 49^th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, p.541-550.

[16]Honnibal M, Dale R, 2009. DAMSEL: the DSTO/Macquarie system for entity-linking. Proc 2^nd Text Analysis Conf.

[17]Hou F, Wang RL, He J, et al., 2020. Improving entity linking through semantic reinforced entity embeddings. Proc 58^th Annual Meeting of the Association for Computational Linguistics, p.6843-6848.

[18]Le P, Titov I, 2018. Improving entity linking by modeling latent relations between mentions. Proc 56^th Annual Meeting of the Association for Computational Linguistics, p.1595-1604.

[19]Li ZZ, Feng DW, Li DS, et al., 2020. Learning to select pseudo labels: a semi-supervised method for named entity recognition. Front Inform Technol Electron Eng, 21(6):903-916.

[20]Luo G, Huang XJ, Lin CY, et al., 2015. Joint entity recognition and disambiguation. Proc Conf on Empirical Methods in Natural Language Processing, p.879-888.

[21]Martins PH, Marinho Z, Martins AFT, 2019. Joint learning of named entity recognition and entity linking. Proc 57^th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, p.190-196.

[22]Medelyan O, Milne D, Legg C, et al., 2009. Mining meaning from Wikipedia. Int J Hum-Comput Stud, 67(9):716-754.

[23]Milne D, Witten IH, 2008. Learning to link with Wikipedia. Proc 17^th ACM Conf on Information and Knowledge Management, p.509-518.

[24]Mulang IO, Singh K, Prabhu C, et al., 2020. Evaluating the impact of knowledge graph context on entity disambiguation models. Proc 29^th ACM Int Conf on Information & Knowledge Management, p.2157-2160.

[25]Nguyen DB, Theobald M, Weikum G, 2016. J-NERD: joint named entity recognition and disambiguation with rich linguistic features. Trans Assoc Comput Linguist, 4:215-229.

[26]Pennington J, Socher R, Manning C, 2014. GloVe: global vectors for word representation. Proc Conf on Empirical Methods in Natural Language Processing, p.1532-1543.

[27]Pershina M, He YF, Grishman R, 2015. Personalized page rank for named entity disambiguation. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.238-243.

[28]Runge A, Hovy E, 2020. Exploring neural entity representations for semantic information. Proc 3^rd BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, p.204-216.

[29]Sevgili O, Shelmanov A, Arkhipov M, et al., 2020. Neural entity linking: a survey of models based on deep learning. https://arxiv.org/abs/2006.00575

[30]Shen W, Wang JY, Han JW, 2015. Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans Knowl Data Eng, 27(2):443-460.

[31]Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Proc 31^st Int Conf on Neural Information Processing Systems, p.6000-6010.

[32]Wang HF, Liu ZQ, 2019. An error recognition method for power equipment defect records based on knowledge graph technology. Front Inform Technol Electron Eng, 20(11):1564-1577.

[33]Wang Z, Zhang JW, Feng JL, et al., 2014. Knowledge graph and text jointly embedding. Proc Conf on Empirical Methods in Natural Language Processing, p.1591-1601.

[34]Wu JS, Zhang RC, Mao YY, et al., 2020. Dynamic graph convolutional networks for entity linking. Proc Web Conf, p.1149-1159.

[35]Xin KX, Hua W, Liu Y, et al., 2019. Entity disambiguation based on parse tree neighbours on graph attention network. Proc 20^th Int Conf on Web Information Systems Engineering, p.523-537.

[36]Xu P, Barbosa D, 2018. Neural fine-grained entity type classification with hierarchy-aware loss. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.16-25.

[37]Xue MG, Cai WM, Su JS, et al., 2019. Neural collective entity linking based on recurrent random walk network learning. Proc 28^th Int Joint Conf on Artificial Intelligence, p.5327-5333.

[38]Yamada I, Shindo H, Takeda H, et al., 2016. Joint learning of the embedding of words and entities for named entity disambiguation. Proc 20^th SIGNLL Conf on Computational Natural Language Learning, p.250-259.

[39]Yang XY, Gu XT, Lin S, et al., 2019. Learning dynamic context augmentation for global entity linking. Proc Conf on Empirical Methods in Natural Language Processing and the 9^th Int Joint Conf on Natural Language Processing, p.271-281.

[40]Zhang HB, Chen Q, Zhang WW, et al., 2022. HSIE: improving named entity disambiguation with hidden semantic information extractor. Proc 14^th Int Conf on Machine Learning and Computing, p.251-257.

[41]Zhong HP, Zhang JW, Wang Z, et al., 2015. Aligning knowledge and text embeddings by entity descriptions. Proc Conf on Empirical Methods in Natural Language Processing, p.267-272.