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Adapting Named Entity Types to New Ontologies in a Microblogging Environment

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Recent Trends and Future Technology in Applied Intelligence (IEA/AIE 2018)

Abstract

Given the potential rise in the amount of user-generated content on social network, research efforts towards Information Extraction have significantly increased, giving leeway to the emergence of numerous Named Entity Recognition (NER) systems. Based on varying application scenarios and/or requirements, different NER systems use different entity classification schemas/ontologies to classify the discovered entity mentions into entity types. Indeed, comparisons and integrations among NER systems become complex. The situation is further worsened due to varying granularity levels of such ontologies used to train the NER systems. This problem has been approached in the state of the art by developing a deterministic manual mapping between concepts belonging to different ontologies. In this paper, we discuss the limitations of these methods and, inspired by a transfer learning paradigm, we propose a novel approach named LearningToAdapt (L2A) to mitigate them. L2A learns to transfer an input probability distribution over a set of ontology types defined in a source domain, into a probability distribution over the types of a new ontology in a target domain. By using the inferred probability distribution, we are able to re-classify the entity mentions using the most probable type in the target domain. Experiments conducted with benchmark data show remarkable performance, suggesting L2A as a promising approach for domain adaptation of NER systems.

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Notes

  1. 1.

    In this paper we use the term ontology interchangeably with the term classification schema.

  2. 2.

    https://github.com/wikilinks/neleval.

  3. 3.

    The experiment have been conducted using default parameters of models implemented in WEKA: www.cs.waikato.ac.nz/ml/weka/.

  4. 4.

    The accuracy reported at 100% could be different from Tables 1 and 2 due to a random seed selection during 10-folds cross-validation.

References

  1. Ritter, A., Clark, S., Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534 (2011)

    Google Scholar 

  2. Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 359–367 (2011)

    Google Scholar 

  3. Rizzo, G., Troncy, R.: NERD: a framework for unifying named entity recognition and disambiguation extraction tools. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 73–76 (2012)

    Google Scholar 

  4. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning, pp. 282–289 (2001)

    Google Scholar 

  5. Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 248–256 (2009)

    Google Scholar 

  6. Rizzo, G., Cano, A.E., Pereira, B., Varga, A.: Making sense of microposts (#Microposts2015) named entity recognition and linking challenge. In: Proceedings of the 5th Workshop on Making Sense of Microposts Co-located with the 24th International World Wide Web Conference, pp. 44–53 (2015)

    Google Scholar 

  7. Rizzo, G., van Erp, M., Plu, J., Troncy, R.: Making sense of microposts (#Microposts2016) named entity recognition and linking challenge. In: Proceedings of the 6th Workshop on Making Sense of Microposts Co-located with the 25th International World Wide Web Conference, pp. 50–59 (2016)

    Google Scholar 

  8. Manchanda, P., Fersini, E., Palmonari, M., Nozza, D., Messina, E.: Towards adaptation of named entity classification. In: Proceedings of the Symposium on Applied Computing, pp. 155–157. ACM (2017)

    Google Scholar 

  9. Daumé III, H.: Frustratingly easy domain adaptation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pp. 256–263 (2007)

    Google Scholar 

  10. Chiticariu, L., Krishnamurthy, R., Li, Y., Reiss, F., Vaithyanathan, S.: Domain adaptation of rule-based annotators for named-entity recognition tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1002–1012 (2010)

    Google Scholar 

  11. Qu, L., Ferraro, G., Zhou, L., Hou, W., Baldwin, T.: Named entity recognition for novel types by transfer learning. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 899–905 (2016)

    Google Scholar 

  12. Eckert, K., Meilicke, C., Stuckenschmidt, H.: Improving ontology matching using meta-level learning. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 158–172. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02121-3_15

    Chapter  Google Scholar 

  13. Shi, F., Li, J., Tang, J., Xie, G., Li, H.: Actively learning ontology matching via user interaction. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 585–600. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04930-9_37

    Chapter  Google Scholar 

  14. Atencia, M., Borgida, A., Euzenat, J., Ghidini, C., Serafini, L.: A formal semantics for weighted ontology mappings. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 17–33. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35176-1_2

    Chapter  Google Scholar 

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Correspondence to Elisabetta Fersini .

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Fersini, E., Manchanda, P., Messina, E., Nozza, D., Palmonari, M. (2018). Adapting Named Entity Types to New Ontologies in a Microblogging Environment. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_76

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  • DOI: https://doi.org/10.1007/978-3-319-92058-0_76

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