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Exploiting anonymous entity mentions for named entity linking

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Abstract

Named entity linking or named entity disambiguation is to link entity mentions to corresponding entities in a knowledge base for resolving the ambiguity of entity mentions. Recently, collective linking methods exploit document-level coherence of the referenced entities by computing a pairwise score between candidates of a pair of named entity mentions (e.g., Raytheon and Boeing) in a document. However, in a document, named entity mentions are significantly less frequent than anonymous entity mentions (e.g., defense contractor and the company). In this paper, we propose a method, DOCument-level Anonymous Entity Type words relatedness (DOC-AET), to exploit the document-level coherence between candidate entities and anonymous entity mentions. We use the anonymous entity type (AET) words to extract anonymous entity mentions. We learn embeddings of AET words from their inter-paragraph co-occurrence matrix; thus, the document-level entity-type relatedness is encoded in the AET word embeddings. Then, we compute the coherence scores between candidate entities and anonymous entity mentions using the AET entity embeddings and document context embeddings. By incorporating such coherence scores for candidates ranking, DOC-AET has achieved new state-of-the-art results on two of the five out-domain test sets for named entity linking.

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Notes

  1. We use italic font to represent anonymous entity mentions.

  2. download from https://drive.google.com/open?id=1OtLnrH4SpDzdNNcuca-DdXCMwsDPsG3B.

  3. https://github.com/lephong/mulrel-nel.

References

  1. Arora S, Li Y, Liang Y, Ma T, Risteski A (2018) Linear algebraic structure of word senses, with applications to polysemy. Trans Assoc Comput Linguist 6:483–495. https://doi.org/10.1162/tacl_a_00034

    Article  Google Scholar 

  2. Bhowmik R, de Melo G (2018) Generating fine-grained open vocabulary entity type descriptions. In: Proceedings of the 56th annual meeting of the association for computational linguistics. https://doi.org/10.18653/v1/P18-1081

  3. Chen S, Wang J, Jiang F, Lin CY (2020) Improving entity linking by modeling latent entity type information. In: Proceedings of the 34th AAAI conference on artificial intelligence, pp 7529–7537 . https://doi.org/10.1609/aaai.v34i05.6251

  4. Cheng X, Roth D (2013) Relational inference for wikification. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1787–1796. https://www.aclweb.org/anthology/D13-1184

  5. Chisholm A, Hachey B (2015) Entity disambiguation with web links. Trans Assoc Comput Linguist 3:145–156. https://doi.org/10.1162/tacl_a_00129

    Article  Google Scholar 

  6. Choi E, Levy Omer, Choi Yejin, Zettlemoyer Luke (2018) Ultra-fine entity typing. In: Proceedings of the 56th annual meeting of the association for computational linguistics. Assoc Comput Linguist, pp 87–96. https://doi.org/10.18653/v1/P18-1009

  7. Cui H, Peng T, Feng L, Bao T, Liu L (2021) Simple question answering over knowledge graph enhanced by question pattern classification. Knowl Inf Syst 63(10):2741–2761

    Article  Google Scholar 

  8. Durrett G, Klein D (2014) A joint model for entity analysis: coreference, typing, and linking. Trans Assoc Comput Linguist

  9. Ensan F, Du W (2019) Ad hoc retrieval via entity linking and semantic similarity. Knowl Inf Syst 58(3):551–583

    Article  Google Scholar 

  10. Gabrilovich Evgeniy, Ringgaard Michael, Subramanya Amarnag (2013) FACC1: Freebase annotation of ClueWeb corpora, version 1 (release date 2013-06-26, format version 1, correction level 0)

  11. Fang W, Zhang J, Wang D, Chen Z, Li M (2016) Entity disambiguation by knowledge and text jointly embedding. In: Proceedings of The 20th SIGNLL conference on computational natural language learning, pp 260–269. Association for computational linguistics, Berlin, Germany. https://doi.org/10.18653/v1/K16-1026.https://www.aclweb.org/anthology/K16-1026

  12. Ganea OE, Ganea M, Lucchi A, Eickhof, C, Hofmann T (2016) Probabilistic bag-of-hyperlinks model for entity linking. In: Proceedings of the 25th international conference on world wide web, pp 927–938. International World Wide Web Conferences Steering Committee

  13. Ganea OE, Hofmann T (2017) Deep joint entity disambiguation with local neural attention. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 2619–2629. Association for Computational Linguistics, Copenhagen, Denmark . https://doi.org/10.18653/v1/D17-1277.https://www.aclweb.org/anthology/D17-1277

  14. Gillick D, Lazic N, Ganchev K, Kirchner J, Huynh D (2014) Contextdependent fine-grained entity type tagging. arXiv preprint arXiv:1412.1820

  15. Globerson A, Lazic N, Chakrabarti S, Subramanya A, Ringaard M, Pereira F (2016) Collective entity resolution with multi-focal attention. In: Proceedings of the 54th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 621–631. https://doi.org/10.18653/v1/P16-1059.https://www.aclweb.org/anthology/P16-1059

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

    Article  Google Scholar 

  17. Gupta N, Singh S, Roth D (2017) Entity linking via joint encoding of types, descriptions, and context. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 2681–2690. Association for Computational Linguistics, Copenhagen, Denmark . https://doi.org/10.18653/v1/D17-1284.https://www.aclweb.org/anthology/D17-1284

  18. Hoffart J, Yosef MA, Bordino I, Fürstenau H, Pinkal M, Spaniol M, Taneva B, Thater S, Weikum G (2011) Robust disambiguation of named entities in text. In: Proceedings of the 2011 conference on empirical methods in natural language processing, pp 782–792. Association for Computational Linguistics . http://www.aclweb.org/anthology/D11-1072

  19. Hoffmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS (2011) Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 541–550. Association for Computational Linguistics, Portland, Oregon, USA. https://www.aclweb.org/anthology/P11-1055

  20. Hou F, Wang R, He J, Zhou Y (2020) Improving entity linking through semantic reinforced entity embeddings. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6843–6848. Association for Computational Linguistics, Online . https://doi.org/10.18653/v1/2020.acl-main.612.https://www.aclweb.org/anthology/2020.acl-main.612

  21. Hou F, Wang R, Zhou Y (2021) Transfer learning for fine-grained entity typing. Knowl Inf Syst 63(4):845–866

    Article  Google Scholar 

  22. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  23. Kouki P, Pujara J, Marcum C, Koehly L, Getoor L (2019) Collective entity resolution in multi-relational familial networks. Knowl Inf Syst 61(3):1547–1581

    Article  Google Scholar 

  24. Lazic N, Subramanya A, Ringgaard M, Pereira F (2015) Plato: a selective context model for entity resolution. Trans Assoc Comput Linguist 3:503–515. https://doi.org/10.1162/tacl_a_00154https://www.aclweb.org/anthology/Q15-1036

  25. Le P, Titov I (2018) Improving entity linking by modeling latent relations between mentions. In: Proceedings of the 56th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp 1595–1604. Association for Computational Linguistics, Melbourne, Australia . https://doi.org/10.18653/v1/P18-1148.https://www.aclweb.org/anthology/P18-1148

  26. Le P, Titov I (2019) Boosting entity linking performance by leveraging unlabeled documents. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1935–1945. Association for Computational Linguistics, Florence, Italy. https://www.aclweb.org/anthology/P19-1187

  27. Ling X, Weld DS (2012) Fine-grained entity recognition. In: Proceedings of association for the advancement of artificial intelligence

  28. Liu M, Zhao Y, Qin B, Liu T (2019) Collective entity linking: a random walk-based perspective. Knowl Inf Syst 60(3):1611–1643

    Article  Google Scholar 

  29. Mikolov T, Sutskever Ilya, Chen Kai, Corrado Greg, Dean Jeffrey (2013) Distributed representations of words and phrases and their compositionality. In: Adv Neural Inf Process Syst, pp 3111–3119

  30. Milne D, Witten IH (2008) Learning to link with wikipedia. In: Proceedings of the 17th ACM conference on information and knowledge management, pp 509–518. ACM

  31. Mu J, Bhat S, Viswanath P (2017) Geometry of polysemy. In: Proceedings of the 5th international conference on learning representations

  32. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press

  33. Pennington J, Socher R, Manning C (2014) GloVe: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543. Association for Computational Linguistics, Doha, Qatar. https://doi.org/10.3115/v1/D14-1162.https://www.aclweb.org/anthology/D14-1162

  34. Phan MC, Sun A, Tay Y, Han J, Li C (2019) Pair-linking for collective entity disambiguation: two could be better than all. IEEE Trans Knowl Data Eng 31(7):1383–1396

    Article  Google Scholar 

  35. Ratinov L, Roth D, Downey D, Anderson M (2011) Local and global algorithms for disambiguation to wikipedia. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1, pp 1375–1384. Association for Computational Linguistics. https://www.aclweb.org/anthology/P11-1138

  36. Shen W, Wang J, Han J (2015) Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans Knowl Data Eng 27(2):443–460

    Article  Google Scholar 

  37. Yaghoobzadeh Y, Adel H, Schutze H (2018) Corpus-level fine-grained entity typing. J Artif Intell Res 61:835–862

    Article  MathSciNet  MATH  Google Scholar 

  38. Yaghoobzadeh Y, Kann K, Hazen TJ, Agirre E, Schütze H (2019) Probing for semantic classes: Diagnosing the meaning content of word embeddings. In: Proceedings of the 57th Annual meeting of the association for computational linguistics, pp 5740–5753. Association for Computational Linguistics, Florence, Italy. https://www.aclweb.org/anthology/P19-1574

  39. Yamada I, Shindo H, Takeda H, Takefuji Y (2016) 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, pp 250–259. Association for Computational Linguistics, Berlin, Germany. https://doi.org/10.18653/v1/K16-1025.https://www.aclweb.org/anthology/K16-1025

  40. Yamada I, Shindo H, Takeda H, Takefuji Y (2017) Learning distributed representations of texts and entities from knowledge base. Trans Assoc Comput Linguist 5:397–411

    Article  Google Scholar 

  41. Yamada I, Washio K, Shindo H, Matsumoto Y (2020) Global entity disambiguation with pretrained contextualized embeddings of words and entities. arXiv preprint arXiv:1909.00426

  42. Yang X, Gu X, Lin S, Tang S, Zhuang Y, Wu F, Chen Z, Hu G, Ren X (2019) 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. Association for Computational Linguistics, Hong Kong, China. https://doi.org/10.18653/v1/D19-1026.https://www.aclweb.org/anthology/D19-1026

  43. Yih Wt, Chang MW, He X, Gao J (2015) Semantic parsing via staged query graph generation: Question answering with knowledge base. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (vol 1: Long Papers), pp 1321–1331. Association for Computational Linguistics, Beijing, China. https://doi.org/10.3115/v1/P15-1128.https://www.aclweb.org/anthology/P15-1128

  44. Zhou X, Miao Y, Wang W, Qin J (2020) A recurrent model for collective entity linking with adaptive features. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, vol. 34, pp. 329–336

  45. Zwicklbauer S, Seifert C, Granitzer M (2016) Robust and collective entity disambiguation through semantic embeddings. In: Proceedings of the 39th international ACM SIGIR conference, pp 425–434. ACM

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Acknowledgements

This work is supported by the 2020 Catalyst: Strategic NZ-Singapore Data Science Research Programme Fund, MBIE, New Zealand.

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Authors and Affiliations

  1. School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand

    Feng Hou & Ruili Wang

  2. Institute of Data Science, National University of Singapore, Queenstown, Singapore

    See-Kiong Ng & Fangyi Zhu

  3. School of Computer Science, University of Auckland, Auckland, New Zealand

    Michael Witbrock

  4. Institute of Governance and School of Politics and Public Administration, Shandong University, Jinan, China

    Xiaoyun Jia

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  1. Feng Hou
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Correspondence to Ruili Wang.

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Hou, F., Wang, R., Ng, SK. et al. Exploiting anonymous entity mentions for named entity linking. Knowl Inf Syst 65, 1221–1242 (2023). https://doi.org/10.1007/s10115-022-01793-3

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