Advertisement

WebEL: Improving Entity Linking with Extra Web Contexts

  • Yiting Wang
  • Zhixu LiEmail author
  • Qiang Yang
  • Zhigang Chen
  • An Liu
  • Guanfeng Liu
  • Lei Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)

Abstract

Entity Linking is the task of determining the identity of textual entity mentions given a predefined Knowledge Graph (KG). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through Web Search Engines. Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where an attention mechanism is applied to help generate high-quality web contexts, while the second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we could combine the two models we proposed to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and global information could effectively improve the performance of entity linking.

Keywords

Entity Linking WSE Attention mechanism 

Notes

Acknowledgments

This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61402313, 61472263), and Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), and this is a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

References

  1. 1.
    Alhelbawy, A., Gaizauskas, R.: Graph ranking for collective named entity disambiguation. In: Meeting of the Association for Computational Linguistics, pp. 75–80 (2014)Google Scholar
  2. 2.
    Basile, P., Caputo, A.: Entity linking for tweets. In: Meeting of the Association for Computational Linguistics, pp. 1304–1311 (2017)Google Scholar
  3. 3.
    Blanco, R., Ottaviano, G., Meij, E.: Fast and space-efficient entity linking in queries. In: Eighth ACM International Conference on Web Search and Data Mining, pp. 179–188 (2015)Google Scholar
  4. 4.
    Bunescu, R.C., Pasca, M.: Using encyclopedic knowledge for named entity disambiguation. In: Conference of the European Chapter of the Association for Computational Linguistics, pp. 9–16 (2006)Google Scholar
  5. 5.
    Cai, R., Wang, H., Zhang, J.: Learning entity representation for named entity disambiguation. In: Meeting of the Association for Computational Linguistics, pp. 30–34 (2013)Google Scholar
  6. 6.
    Cheng, X., Roth, D.: Relational inference for wikification. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1787–1796 (2013)Google Scholar
  7. 7.
    Chisholm, A., Hachey, B.: Entity disambiguation with web links. Trans. Assoc. Comput. Linguist. 3, 145–156 (2015)CrossRefGoogle Scholar
  8. 8.
    Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: EMNLP-CoNLL 2007, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, Czech Republic, 28–30 June 2007, pp. 708–716 (2007)Google Scholar
  9. 9.
    Fang, W., Zhang, J., Wang, D., Chen, Z., Li, M.: Entity disambiguation by knowledge and text jointly embedding. In: SIGNLL Conference on Computational Natural Language Learning., pp. 260–269 (2016)Google Scholar
  10. 10.
    Francislandau, M., Durrett, G., Klein, D.: Capturing semantic similarity for entity linking with convolutional neural networks. In: North American Chapter of the Association for Computational Linguistics, pp. 1256–1261 (2016)Google Scholar
  11. 11.
    Ganea, O.E., Ganea, M., Lucchi, A., Eickhoff, C., Hofmann, T.: Probabilistic bag-of-hyperlinks model for entity linking. In: International World Wide Web Conferences, pp. 927–938 (2015)Google Scholar
  12. 12.
    Ganea, O.E., Hofmann, T.: Deep joint entity disambiguation with local neural attention. arXiv preprint arXiv:1704.04920 (2017)
  13. 13.
    Gang, Z., Zong-Min, M.A., Kan, H.M., Niu, L.Q.: Texture feature extraction approach using co-occurrence matrix. J. Shenyang Univ. Technol. 32(2), 192–195+211 (2010)Google Scholar
  14. 14.
    Globerson, A., Lazic, N., Chakrabarti, S., Subramanya, A., Ringaard, M., Pereira, F.: Collective entity resolution with multi-focal attention. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 621–631 (2016)Google Scholar
  15. 15.
    Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 765–774 (2011)Google Scholar
  16. 16.
    Hoffart, J., et al.: Robust disambiguation of named entities in text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 782–792. Association for Computational Linguistics (2011)Google Scholar
  17. 17.
    Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L.S., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 541–550 (2011)Google Scholar
  18. 18.
    Huu, T., Fauceglia, N., Muro, M.R., Hassanzadeh, O., Gliozzo, A.M., Sadoghi, M.: Joint learning of local and global features for entity linking via neural networks. In: The International Conference on Computational Linguistics (2016)Google Scholar
  19. 19.
    Ji, H., Grishman, R., Dang, H.T., Griffitt, K., Ellis, J.: Overview of the TAC 2010 knowledge base population track. In: Text Analysis Conference (2009)Google Scholar
  20. 20.
    Landgraf, A.J., Bellay, J.: word2vec skip-gram with negative sampling is a weighted logistic PCA. Computation and Language (2017)Google Scholar
  21. 21.
    Le, P., Titov, I.: Improving entity linking by modeling latent relations between mentions. arXiv preprint arXiv:1804.10637 (2018)
  22. 22.
    Lei, K., Deng, Y., Zhang, B., Shen, Y.: Open domain question answering with character-level deep learning models. In: International Symposium on Computational Intelligence and Design, pp. 30–33 (2018)Google Scholar
  23. 23.
    Liu, F.H., Gu, L., Gao, Y., Picheny, M.: Use of statistical n-gram models in natural language generation for machine translation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003, Proceedings, vol. 1, pp. I-636-I-639 (2003)Google Scholar
  24. 24.
    Liu, G., Wang, Y., Orgun, M.A.: Finding k optimal social trust paths for the selection of trustworthy service providers in complex social networks. IEEE Trans. Serv. Comput. 6(2), 152–167 (2013)CrossRefGoogle Scholar
  25. 25.
    Liu, G., Yan, W., Orgun, M.A.: Optimal social trust path selection in complex social networks. In: Twenty-Fourth AAAI Conference on Artificial Intelligence (2010)Google Scholar
  26. 26.
    Luo, A., Gao, S., Xu, Y.: Deep semantic match model for entity linking using knowledge graph and text. Procedia Comput. Sci. 129, 110–114 (2018)CrossRefGoogle Scholar
  27. 27.
    Mcnamee, P., Dang, H.T.: Overview of the TAC 2009 knowledge base population track. In: Text Analysis Conference (2009)Google Scholar
  28. 28.
    Mihalcea, R., Csomai, A.: Wikify! linking documents to encyclopedic knowledge. In: Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 233–242 (2007)Google Scholar
  29. 29.
    Pauls, A., Dan, K.: Faster and smaller n-gram language models. In: The Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, Portland, Oregon, USA, 19–24 June 2011, pp. 258–267 (2012)Google Scholar
  30. 30.
    Phan, M.C., Sun, A., Yi, T., Han, J., Li, C.: NeuPL: attention-based semantic matching and pair-linking for entity disambiguation. In: CIKM (2017)Google Scholar
  31. 31.
    Ratinov, L.A., Dan, R., Downey, D., Anderson, M.: Local and global algorithms for disambiguation to wikipedia. In: The Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, Portland, Oregon, USA, 19–24 June 2011, pp. 1375–1384 (2011)Google Scholar
  32. 32.
    Sun, Y., Lin, L., Tang, D., Yang, N., Ji, Z., Wang, X.: Modeling mention, context and entity with neural networks for entity disambiguation. In: International Conference on Artificial Intelligence, pp. 1333–1339 (2015)Google Scholar
  33. 33.
    Wallach, H.M.: Topic modeling: beyond bag-of-words. In: International Conference on Machine Learning, pp. 977–984 (2006)Google Scholar
  34. 34.
    Witten, I.H., Milne, D.N.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proceedings of the First AAAI Workshop on Wikipedia and Artificial Intelligence (2008)Google Scholar
  35. 35.
    Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. In: Conference on Computational Natural Language Learning, pp. 250–259 (2016)Google Scholar
  36. 36.
    Yih, W.T., Chang, M.W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing, pp. 1321–1331 (2015)Google Scholar
  37. 37.
    Liu, M., Chen, L., Liu, B., Zheng, G., Zhang, X.: DBpedia-based entity linking via greedy search and adjusted Monte Carlo random walk. ACM Trans. Inf. Syst. 36(2), 16 (2017) CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yiting Wang
    • 1
  • Zhixu Li
    • 1
    • 2
    Email author
  • Qiang Yang
    • 4
  • Zhigang Chen
    • 3
  • An Liu
    • 1
  • Guanfeng Liu
    • 5
  • Lei Zhao
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.iFLYTEK ResearchSuzhouChina
  3. 3.State Key Laboratory of Cognitive Intelligence, iFLYTEKHefeiPeople’s Republic of China
  4. 4.King Abdullah University of Science and TechnologyJeddahSaudi Arabia
  5. 5.Macquarie UniversitySydneyAustralia

Personalised recommendations