Iris Hendrickx
Antal van den Bosch
ABSTRACT
We present a memory-based named-entity recognition system that chunks and labels named entities in a oneshot task. Training and testing on CoNLL-2003 shared task data, we measure the effects of three extensions. First, we incorporate features that signal the presence of wordforms in external, language-specific seed (gazetteer) lists. Second, we build a second-stage stacked classifier that corrects first-stage output errors. Third, we add selected instances from classified unannotated data to the training material. The system that incorporates all attains an overall F-rate on the final test set of 78.20 on English and 63.02 on German.
- R. H. Baayen, R. Piepenbrock, and H. van Rijn. 1993. The CELEX lexical data base on CD-ROM. Linguistic Data Consortium, Philadelphia, PA.Google Scholar
- S. Buchholz and A. Van den Bosch. 2000. Integrating seed names and n-grams for a named entity list and classifier. In LREC-2000 (Second International Conference on Language Resources and Evaluation) Proceedings. Vol. II, pages 1215--1221.Google Scholar
- X. Carreras, L. Marques, and L. Padro. 2002. Named entity extraction using AdaBoost. In Proceedings of CoNLL-2002, pages 167--170. Google ScholarDigital Library
- S. Cucerzan and D. Yarowsky. 1999. Language independent named entity recognition combining morphological and contextual evidence. In Proceedings of 1999 Joint SIGDAT Conference on EMNLP and VLC.Google Scholar
- W. Daelemans, J. Zavrel, K. Van der Sloot, and A. Van den Bosch. 2002. TiMBL: Tilburg Memory Based Learner, version 4.3, reference guide. Technical Report ILK-0210, ILK, Tilburg University.Google Scholar
- R. Kohavi and G. John. 1997. Wrappers for feature subset selection. Artificial Intelligence Journal, 97(1--2):273--324. Google ScholarDigital Library
- F. Provost, D. Jensen, and T. Oates. 1999. Efficient progressive sampling. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pages 23--32. Google ScholarDigital Library
- Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the conll-2003 shared task: Language-independent named entity recognition. In Proceedings of CoNLL-2003. Edmonton, Canada. Google ScholarDigital Library
- A. Van den Bosch and W. Daelemans. 1999. Memory-based morphological analysis. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 285--292, New Brunswick, NJ. ACL. Google ScholarDigital Library
- J. Veenstra. 1998. Fast np chunking using memory-based learning techniques. In Proceedings of Bene-learn 1998, pages 71--79.Google Scholar
- D. H. Wolpert. 1992. On overfitting avoidance as bias. Technical Report SFI TR 92-03-5001, The Santa Fe Institute.Google Scholar
- D. Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In ACL33, pages 189--196, Cambridge, MA. Google ScholarDigital Library
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