Abstract
Named Entity Recognition has been a hot topic in Natural Language Processing for more than fifteen years. A number of systems for various languages have been developed using different approaches and based on different named entity schemes and tagging strategies. We present the NERosetta web application that can be used for comparison of these various approaches applied to aligned texts (bitexts). In order to illustrate its functionalities, we have used one literary text, its 7 bitexts involving 5 languages and 5 different NER systems. We present some preliminary results and give guidelines for further development.
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.
- 2.
- 3.
A tool developed by P. Bonhomme, T. M. H. Nguyen and S. O’Rourke, http://led.loria.fr/outils/ALIGN/align.html.
- 4.
- 5.
We have used the version of the cascade and e-dictionaries from February 2012.
- 6.
- 7.
- 8.
- 9.
References
Béchet, F., Sagot, B., Stern, R., et al.: Coopération de méthodes statistiques et symboliques pour l’adaptation non-supervisée d’un système d’étiquetage en entités nommées. In: TALN 2011-Traitement Automatique des Langues Naturelles (2011)
Burnard, L., Bauman, S.: TEI P5: Guidelines for Electronic Text Encoding and Interchange (2008)
Chinchor, N.: MUC-6 Named Entity Task Definition (Version 2.1) (1995)
Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting of the ACL, pp. 363–370 (2005)
Grishman, R., Sundheim, B.: Message understanding conference-6: a brief history. COLING, vol. 1, pp. 466–471. Association for Computational Linguistics, Stroudsburg (1996)
Krstev, C., Obradović, I., Utvić, M., Vitas, D.: A system for named entity recognition based on local grammars. J. Logic Comput. (2013). doi:10.1093/logcom/exs079
Kyriacopoulou, T., Martineau, C., Mavropoulos, T.: Les noms propres de personne en français et en grec: reconnaissance, extraction et enrichissement de dictionnaire. In: Proceedings of the 30th Conference on Lexis and Grammar, LGC 2011, Cyprus (2011)
Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: Proceedings of the 49th Annual Meeting of the ACL: Human Language Technologies, vol. 1, pp. 359–367 (2011)
Ljubešić, N., Stupar, M., Jurić, T., Agić, Ž.: Combining available datasets for building named entity recognition models of Croatian and Slovene. In: Slovenščina 2.0: Empirical, Applied and Interdisciplinary Research (2013) (in press)
Maurel, D., Friburger, N., Antoine, J.-Y., Eshkol, I., Nouvel, D., et al.: Cascades de transducteurs autour de la reconnaissance des entités nommes. Traitement Automatique des Langues 52(1), 69–96 (2011)
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. In: Sekine, S., Ranchhod, E. (eds.) Named Entities: Recognition, Classification and Use, pp. 3–28. John Benjamins Pub. Co., Amsterdam/Philadelphia (2009)
Nadeau, D., Turney, P., Matwin, S.: Unsupervised named-entity recognition: generating gazetteers and resolving ambiguity. In: 19th Canadian Conference on Artificial Intelligence, Québec City, Québec, Canada (2006)
Pustejovsky, J., Lee, K., Bunt, H., Romary, L.: ISO-TimeML: an international standard for semantic annotation. In: 7th LREC 2010. ELRA, Valletta (2010)
Rehm, G., Uszkoreit, H. (eds.): META-NET White Paper Series. Springer, Heidelberg (2012)
Rosset, S., Grouin, C., Zweigenbaum, P.: Entités Nommées Structurées: guide d’annotation Quaero. LIMSI-CNRS, Orsay (2011)
Sekine, S., Nobata, C.: Definition, dictionaries and tagger for extended named entity hierarchy. In: LREC, Lisbon, Portugal (2004)
Steinberger, R., Bruno, P.: Cross-lingual named entity recognition. In: Sekine, S., Ranchhod, E. (eds.) Named Entities: Recognition, Classification and Use, pp. 137–164. John Benjamins Pub. Co. (2009)
Tatar, S., Cicekli, I.: Automatic rule learning exploiting morphological features for named entity recognition in Turkish. J. Inf. Sci. 37(2), 137–151 (2011)
Acknowledgments
This research was conducted through the project 178006 financed by the Serbian Ministry of Education, Science and Technological Development.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Krstev, C., Zečević, A., Vitas, D., Kyriacopoulou, T. (2016). NERosetta for the Named Entity Multi-lingual Space. In: Vetulani, Z., Uszkoreit, H., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2013. Lecture Notes in Computer Science(), vol 9561. Springer, Cham. https://doi.org/10.1007/978-3-319-43808-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-43808-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43807-8
Online ISBN: 978-3-319-43808-5
eBook Packages: Computer ScienceComputer Science (R0)