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LoG: a locally-global model for entity disambiguation

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Abstract

Entity disambiguation (ED) aims to link textual mentions in a document to the correct named entities in a knowledge base (KB). Although global ED models usually outperform local models by collectively linking mentions based on the topical coherence assumption, they may still incur incorrect entity assignment when a document contains multiple topics. Therefore, we propose a Locally-Global model (LoG) for ED which extracts global features locally, i.e., among a limited number of neighboring mentions, to combine the respective superiority of both models. In particular, we derive mention neighbors according to the syntactic distance on a dependency parse tree, and propose a tree connection method CoSimTC to measure the cross-tree distance between mentions. We also recognize the importance of keywords in a document for collective entity disambiguation, which reveal the central topic information of the document. Hence, we propose a keyword extraction method Sent2Word to detect keywords of each document. Furthermore, we extend the Graph Attention Network (GAT) to integrate both local and global features to produce a discriminative representation for each candidate entity. Our experimental results on six widely-adopted public datasets demonstrate better performance compared with state-of-the-art ED approaches. The high efficiency of the LoG model further verifies its feasibility in practice.

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References

  1. Alhelbawy, A., Gaizauskas, R.: Graph ranking for collective named entity disambiguation. In: ACL, vol. 2, pp. 75–80 (2014)

  2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  3. Cao, Y., Hou, L., Li, J., Liu, Z.: Neural collective entity linking. In: COLING, pp. 675–686 (2018)

  4. Chen, S., Wang, J., Jiang, F., Lin, C.Y.: Improving entity linking by modeling latent entity type information. In: AAAI (2020)

  5. Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: EMNLP-CoNLL (2007)

  6. Dredze, M., McNamee, P., Rao, D., Gerber, A., Finin, T.: Entity disambiguation for knowledge base population. In: COLING, pp. 277–285 (2010)

  7. Fang, Z., Cao, Y., Li, Q., Zhang, D., Zhang, Z., Liu, Y.: Joint entity linking with deep reinforcement learning. In: The World Wide Web Conference, pp. 438–447 (2019)

  8. Francis-Landau, M., Durrett, G., Klein, D.: Capturing semantic similarity for entity linking with convolutional neural networks. In: NAACL, pp. 1256–1261 (2016)

  9. Gabrilovich, E., Ringgaard, M., Subramanya, A.: Facc1: Freebase annotation of clueweb corpora Note: http://lemurproject.org/clueweb09/FACC1/ Cited by 5 (2013)

  10. Ganea, O.E., Hofmann, T.: Deep joint entity disambiguation with local neural attention. In: EMNLP, pp. 2619–2629 (2017)

  11. Ganea, O.E., Ganea, M., Lucchi, A., Eickhoff, C., Hofmann, T.: Probabilistic bag-of-hyperlinks model for entity linking. In: WWW, pp. 927–938 (2016)

  12. Guo, Z., Barbosa, D.: Robust entity linking via random walks. In: CIKM, pp. 499–508. ACM (2014)

  13. Guo, Z., Barbosa, D.: Robust named entity disambiguation with random walks. Semant. Web 9(4), 459–479 (2018)

    Article  Google Scholar 

  14. Gupta, N., Singh, S., Roth, D.: Entity linking via joint encoding of types, descriptions, and context. In: EMNLP, pp. 2681–2690 (2017)

  15. Hachey, B., Radford, W., Curran, J.R.: Graph-based named entity linking with wikipedia. In: WISE, pp. 213–226. Springer (2011)

  16. Han, X., Sun, L., Zhao, J.: Collective entity linking in Web text: a graph-based method. In: SIGIR, pp. 765–774. ACM (2011)

  17. He, Z., Liu, S., Li, M., Zhou, M., Zhang, L., Wang, H.: Learning entity representation for entity disambiguation. In: ACL(Short Papers), vol. 2, pp. 30–34 (2013)

  18. Hoffart, J., Yosef, M.A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., Weikum, G.: Robust disambiguation of named entities in text. In: EMNLP, pp. 782–792 (2011)

  19. Hua, W., Wang, Z., Wang, H., Zheng, K., Zhou, X.: Short text understanding through lexical-semantic analysis. In: ICDE, pp. 495–506 (2015)

  20. Hua, W., Zheng, K., Zhou, X.: Microblog entity linking with social temporal context. In: SIGMOD, pp. 1761–1775 (2015)

  21. Hua, W., Wang, Z., Wang, H., Zheng, K., Zhou, X.: Understand short texts by harvesting and analyzing semantic knowledge. IEEE Trans. Knowl. Data Eng. 29(3), 499–512 (2017)

    Article  Google Scholar 

  22. Kipf, T. N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2016)

  23. Le, P., Titov, I.: Improving entity linking by modeling latent relations between mentions. In: ACL, pp. 1595–1604 (2018)

  24. Lehmann, J., Monahan, S., Nezda, L., Jung, A., Shi, Y.: Lcc approaches to knowledge base population at tac 2010. In: TAC (2010)

  25. Liu, X., Li, Y., Wu, H., Zhou, M., Wei, F., Lu, Y.: Entity linking for tweets. In: ACL, vol. 1, pp. 1304–1311 (2013)

  26. Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In: CIKM, pp. 233–242. ACM (2007)

  27. Mueller, D., Durrett, G.: Effective use of context in noisy entity linking. In: EMNLP, pp. 1024–1029 (2018)

  28. Nie, F., Cao, Y., Wang, J., Lin, C.Y., Pan, R.: Mention and entity description co-attention for entity disambiguation. In: AAAI (2018)

  29. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the Web. Tech. rep., Stanford InfoLab (1999)

  30. Pappu, A., Blanco, R., Mehdad, Y., Stent, A., Thadani, K.: Lightweight multilingual entity extraction and linking. In: WSDM, pp. 365–374. ACM (2017)

  31. Pennington, J., Socher, R., Manning, C.: Glove: Global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)

  32. Phan, M.C., Sun, A., Tay, Y., Han, J., Li, C.: Neupl: Attention-based semantic matching and pair-linking for entity disambiguation. In: CIKM, pp. 1667–1676. ACM (2017)

  33. Ratinov, L., Roth, D., Downey, D., Anderson, M.: Local and global algorithms for disambiguation to wikipedia. In: ACL, pp. 1375–1384 (2011)

  34. Seidman, S.B.: Network structure and minimum degree. Social Netw. 5(3), 269–287 (1983)

    Article  MathSciNet  Google Scholar 

  35. Tixier, A., Malliaros, F., Vazirgiannis, M.: A graph degeneracy-based approach to keyword extraction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1860–1870 (2016)

  36. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)

  37. Xin, K., Hua, W., Liu, Y., Zhou, X.: Entity disambiguation based on parse tree neighbours on graph attention network. In: 523–537 (2019)

  38. Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. In: CoNLL, p 250 (2016)

  39. Yang, X., Gu, X., Lin, S., Tang, S., Zhuang, Y., Wu, F., Chen, Z., Hu, G., Ren, X.: 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), pp. 271–281 (2019)

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  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Kexuan Xin, Wen Hua, Yu Liu & Xiaofang Zhou

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  1. Kexuan Xin
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  2. Wen Hua
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  3. Yu Liu
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  4. Xiaofang Zhou
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Correspondence to Wen Hua.

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This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2019

Guest Editors: Reynold Cheng, Nikos Mamoulis, and Xin Huang

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Xin, K., Hua, W., Liu, Y. et al. LoG: a locally-global model for entity disambiguation. World Wide Web 24, 351–373 (2021). https://doi.org/10.1007/s11280-020-00845-4

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