Abstract
Entity linking aims to assign a unique identity to entities mentioned in text given a predefined Knowledge Base. Previous works address this task based on the local or global features or the combination of them. However, they are faced with the following problems: 1) For the local features based models, their decisions tend to choose entities with high external knowledge support due to the unbalanced training data and supporting score combination strategy. 2) For the global features based methods, the collective entity linking methods suffer from high computational complexity while the sequential decision model may ignore the correlation between mentions. To tackle the problem of local models, this paper proposes to leverage graph convolutional network for entity embeddings, which could integrate global semantic information and latent relation between entities. We also utilize multi-hop attention mechanism to strengthen the expression of mention context and balance the contributions of mention context and external knowledge. To tackle the problem of global methods, we put forward a global sequential inference model with graph-based search algorithm to model the coherence between mentions with low computation cost. Extensive experiments show that our model could achieve competitive results on multiple standard datasets.
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References
Bunescu, R.C., Pasca, M.: Using encyclopedic knowledge for named entity disambiguation. In: EACL 2006, 11st Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, April 3–7, 2006, Trento, Italy (2006). https://www.aclweb.org/anthology/E06-1002/
Camacho-Collados, J., Bovi, C.D., Raganato, A., Navigli, R.: Sensedefs: amultilingual corpus of semantically annotated textual definitions -exploiting multiple languages and resources jointly for high-quality wordsense disambiguation and entity linking. Lang. Resour. Eval. 53(2), 251–278 (2019). https://doi.org/10.1007/s10579-018-9421-3
Chen, H., Wei, B., Liu, Y., Li, Y., Yu, J., Zhu, W.: Bilinear joint learning of word and entity embeddings for entity linking. Neurocomputing 294, 12–18 (2018). https://doi.org/10.1016/j.neucom.2017.11.064
Francis-Landau, M., Durrett, G., Klein, D.: Capturing semantic similarity for entity linking with convolutional neural networks. In: NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12–17, 2016, pp. 1256–1261 (2016). https://www.aclweb.org/anthology/N16-1150/
Ganea, O., Ganea, M., Lucchi, A., Eickhoff, C., Hofmann, T.: Probabilistic bag-of-hyperlinks model for entity linking. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11–15, 2016, pp. 927–938 (2016). https://doi.org/10.1145/2872427.2882988
Ganea, O., Hofmann, T.: Deep joint entity disambiguation with local neural attention. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9–11, 2017, pp. 2619–2629 (2017). https://www.aclweb.org/anthology/D17-1277/
Globerson, A., Lazic, N., Chakrabarti, S., Subramanya, A., Ringard, M., Pereira, F.: Collective entity resolution with multi-focal attention. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7–12, 2016, Berlin, Germany, Volume 1: Long Papers (2016). https://doi.org/10.18653/v1/p16-1059
Guo, Z., Barbosa, D.: Entity linking with a unified semantic representation. In: 23rd International World Wide Web Conference, WWW ’14, Seoul, Republic of Korea, April 7–11, 2014, Companion Volume, pp. 1305–1310 (2014). https://doi.org/10.1145/2567948.2579705
Guo, Z., Barbosa, D.: Robust named entity disambiguation with random walks. Semantic Web 9(4), 459–479 (2018). https://doi.org/10.3233/SW-170273
He, Z., Liu, S., Li, M., Zhou, M., Zhang, L., Wang, H.: Learning entity representation for entity disambiguation. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, 4–9 August 2013, Sofia, Bulgaria, Volume 2: Short Papers, pp. 30–34 (2013). https://www.aclweb.org/anthology/P13-2006/
Le, P., Titov, I.: Improving entity linking by modeling latent relations between mentions. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15–20, 2018, Volume 1: Long Papers. pp. 1595–1604 (2018). https://doi.org/10.18653/v1/P18-1148
Mulang, I.O., et al.: Context-aware entity linking with attentive neural networks on Wikidata knowledge graph. CoRR abs/1912.06214 (2019). http://arxiv.org/abs/1912.06214
Nguyen, D.B., Hoffart, J., Theobald, M., Weikum, G.: Aida-light: high-throughput named-entity disambiguation. In: Proceedings of the Workshop on Linked Data on the Web co-located with the 23rd International World Wide Web Conference (WWW 2014), Seoul, Korea, April 8, 2014 (2014). http://ceur-ws.org/Vol-1184/ldow2014_paper_03.pdf
Nguyen, T.H., Fauceglia, N.R., Rodriguez-Muro, M., Hassanzadeh, O., Gliozzo, A.M., Sadoghi, M.: Joint learning of local and global features for entity linking via neural networks. In: COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11–16, 2016, Osaka, Japan, pp. 2310–2320 (2016). https://www.aclweb.org/anthology/C16-1218/
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25–29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL. pp. 1532–1543 (2014). https://doi.org/10.3115/v1/d14-1162
Phan, M.C., Sun, A., Tay, Y., Han, J., Li, C.: Pair-linking for collectiveentity disambiguation: Two could be better than all. IEEE Trans. Knowl. Data Eng. 31(7), 1383–1396 (2019). https://doi.org/10.1109/TKDE.2018.2857493
Ratinov, L., Roth, D., Downey, D., Anderson, M.: Local and global algorithms for disambiguation to Wikipedia. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19–24 June, 2011, Portland, Oregon, USA, pp. 1375–1384 (2011). https://www.aclweb.org/anthology/P11-1138/
Wu, J., Zhang, R., Mao, Y., Guo, H., Soflaei, M., Huai, J.: Dynamic graph convolutional networks for entity linking. In: Huang, Y., King, I., Liu, T., van Steen, M. (eds.) WWW 2020: The Web Conference 2020, Taipei, Taiwan, April 20–24, 2020, pp. 1149–1159. ACM / IW3C2 (2020). https://doi.org/10.1145/3366423.3380192
Yaghoobzadeh, Y., Schütze, H.: Corpus-level fine-grained entity typing using contextual information. CoRR abs/1606.07901 (2016). http://arxiv.org/abs/1606.07901
Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, August 11–12, 2016, pp. 250–259 (2016). https://doi.org/10.18653/v1/k16-1025
Yang, X., et al.: Learning dynamic context augmentation for global entity linking. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3–7, 2019, pp. 271–281 (2019). https://doi.org/10.18653/v1/D19-1026
Acknowledgments
This research is partially supported by National Key R&D Program of China (No. 2018AAA0101900), the Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China (Grant No. 62072323, 61632016, 61836007), Natural Science Foundation of Jiangsu Province (No. BK20191420), Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), and the Suda-Toycloud Data Intelligence Joint Laboratory.
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Deng, Z., Li, Z., Yang, Q., Liu, Q., Chen, Z. (2020). Improving Entity Linking with Graph Networks. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_25
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DOI: https://doi.org/10.1007/978-3-030-62005-9_25
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