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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In this paper we use the term ontology interchangeably with the term classification schema.
- 2.
- 3.
The experiment have been conducted using default parameters of models implemented in WEKA: www.cs.waikato.ac.nz/ml/weka/.
- 4.
References
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)
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)
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)
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)
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)
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)
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)
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)
Daumé III, H.: Frustratingly easy domain adaptation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pp. 256–263 (2007)
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)
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)
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
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-92058-0_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92057-3
Online ISBN: 978-3-319-92058-0
eBook Packages: Computer ScienceComputer Science (R0)