Abstract
The development of highly accurate Named Entity Recognition (NER) systems can be beneficial to a wide range of Human Language Technology applications. In this paper we introduce three heuristics that exploit a variety of knowledge sources (the World Wide Web, Wikipedia and WordNet) and are capable of improving further a state-of-the-art multilingual and domain independent NER system. Moreover we describe our investigations on entity recognition in simulated speech-to-text output. Our web-based heuristics attained a slight improvement over the best results published on a standard NER task, and proved to be particularly effective in the speech-to-text scenario.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bikel, D.M., Schwartz, R.L., Weischedel, R.M.: An algorithm that learns what’s in a name. Machine Learning 34(1-3), 211–231 (1999)
Bunescu, R., Paşca, M.: Using Encyclopedic Knowledge for Named Entity Disambiguation. In: Proceedings of 11th Conference of the European Chapter of the Association for Computational Linguistics (2006)
Carreras, X., Márques, L., Padró, L.: Named Entity Extraction using AdaBoost Proceedings of CoNLL-2002, Taipei, Taiwan, pp. 167–170 (2002)
Chinchor, N.: MUC-7 Named Entity Task Definition. In: Proceedings of Seventh MUC (1998)
Cimiano, P., Handschuh, S., Staab, S.: Towards the self-annotating web. In: Proceedings of the 13th WWW Conference (2004)
Etzioni, O., Cafarella, M., Downey, D., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised named-entity extraction from the web: an experimental study. Artificial Intelligence 165(1), 91–134 (2005)
Florian, R., Ittycheriah, A., Jing, H., Zhang, T.: Named Entity Recognition through Classifier Combination. In: Proceedings of CoNLL 2003 (2003)
Quinlan, R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)
Shapire, R.E.: The Strength of Weak Learnability. Machine Learnings 5, 197–227 (1990)
Szarvas, Gy., Farkas, R., Kocsor, A.: A multilingual named entity recognition system using boosting and c4.5 decision tree learning algorithms. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds.) DS 2006. LNCS (LNAI), vol. 4265, pp. 267–278. Springer, Heidelberg (2006)
Szarvas, G., Farkas, R., Iván, S., Kocsor, A., Busa-Fekete, R.: An iterative method for the de-identification of structured medical text. Workshop on Challenges in Natural Language Processing for Clinical Data (2006)
Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL 2003 Shared Task: Language-Independent Named Entity Recognition. In: Proceedings of CoNLL-2003 (2003)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Farkas, R., Szarvas, G., Ormándi, R. (2007). Improving a State-of-the-Art Named Entity Recognition System Using the World Wide Web. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-73435-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73434-5
Online ISBN: 978-3-540-73435-2
eBook Packages: Computer ScienceComputer Science (R0)