Abstract
Numerous entity linking systems are addressing the entity recognition problem by using off-the-shelf NER systems. It is, however, a difficult task to select which specific model to use for these systems, since it requires to judge the level of similarity between the datasets which have been used to train models and the dataset at hand to be processed in which we aim to properly recognize entities. In this paper, we present the newest version of ADEL, our adaptive entity recognition and linking framework, where we experiment with an hybrid approach mixing a model combination method to improve the recognition level and to increase the efficiency of the linking step by applying a filter over the types. We obtain promising results when performing a 4-fold cross validation experiment on the OKE 2016 challenge training dataset. We also demonstrate that we achieve better results that in our previous participation on the OKE 2015 test set. We finally report the results of ADEL on the OKE 2016 test set and we present an error analysis highlighting the main difficulties of this challenge.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
PREFIX db: <http://dbpedia.org/resource/>.
References
Ferragina, P., Scaiella, U.: TAGME: on-the-fly annotation of short text fragments (by wikipedia entities). In: 19th ACM Conference on Information and Knowledge Management (CIKM) (2010)
Finkel, J., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: 43rd Annual Meeting on Association for Computational Linguistics (2005)
Hellmann, S., Lehmann, J., Auer, S., BrĆ¼mmer, M.: Integrating NLP using linked data. In: Alani, H., et al. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 98ā113. Springer, Heidelberg (2013)
Hoffart, J., Altun, Y., Weikum, G.: Discovering emerging entities with ambiguous names. In: 23rd World Wide Web Conference (WWW) (2014)
Huang, H., Heck, L., Ji, H.: Leveraging deep neural networks and knowledge graphs for entity disambiguation. CoRR (2015)
Ilievski, F., Rizzo, G., van Erp, M., Plu, J., Troncy, R.: Context-enhanced adaptive entity linking. In: 10th International Conference on Language Resources and Evaluation (LREC) (2016)
Ling, X., Singh, S., Weld, D.S.: Design challenges for entity linking. Trans. Assoc. Comput. Linguist. (TACL) 3, 315ā328 (2015)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations (2014)
Plu, J.: Knowledge extraction in web media: at the frontier of NLP, machine learning and semantics. In: 25th World Wide Web Conference (WWW), Ph.D. Symposium (2016)
Plu, J., Rizzo, G., Troncy, R.: A hybrid approach for entity recognition and linking. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds.) Semantic Web Evaluation Challenges. Communications in Computer and Information Science, vol. 548, pp. 28ā39. Springer, Switzerland (2015)
Plu, J., Rizzo, G., Troncy, R.: Revealing entities from textual documents using a hybrid approach. In: 3rd NLP&DBpedia International Workshop (2015)
Speck, R., Ngonga Ngomo, A.-C.: Ensemble learning for named entity recognition. In: Mika, P., et al. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 519ā534. Springer, Heidelberg (2014)
Tjong Kim Sang, E.F., Meulder, F.D.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: 17th Conference on Computational Natural Language Learning (CoNLL) (2003)
Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (2003)
Usbeck, R., Rƶder, M., Ngonga Ngomo, A.-C., Baron, C., Both, A., BrĆ¼mmer, M., Ceccarelli, D., Cornolti, M., Cherix, D., Eickmann, B., Ferragina, P., Lemke, C., Moro, A., Navigli, R., Piccinno, F., Rizzo, G., Sack, H., Speck, R., Troncy, R., Waitelonis, J., Wesemann, L.: GERBIL: general entity annotator benchmarking framework. In: 24th World Wide Web Conference (WWW) (2015)
van Erp, M., Mendes, P.N., Paulheim, H., Ilievski, F., Plu, J., Rizzo, G., Waitelonis, J., Linking, E.E.: An analysis of current benchmark datasets and a roadmap for NG a better job. In: 10th International Conference on Language Resources and Evaluation (LREC) (2016)
Acknowledgments
This work was partially supported by the innovation activity 3cixty (14523) of EIT Digital and by the European Unionās H2020 Framework Programme via the FREME Project (644771).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Plu, J., Rizzo, G., Troncy, R. (2016). Enhancing Entity Linking by Combining NER Models. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds) Semantic Web Challenges. SemWebEval 2016. Communications in Computer and Information Science, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-46565-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-46565-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46564-7
Online ISBN: 978-3-319-46565-4
eBook Packages: Computer ScienceComputer Science (R0)