Joint bilingual name tagging for parallel corpora

ABSTRACT

Traditional isolated monolingual name taggers tend to yield inconsistent results across two languages. In this paper, we propose two novel approaches to jointly and consistently extract names from parallel corpora. The first approach uses standard linear-chain Conditional Random Fields (CRFs) as the learning framework, incorporating cross-lingual features propagated between two languages. The second approach is based on a joint CRFs model to jointly decode sentence pairs, incorporating bilingual factors based on word alignment. Experiments on Chinese-English parallel corpora demonstrated that the proposed methods significantly outperformed monolingual name taggers, were robust to automatic alignment noise and achieved state-of-the-art performance. With only 20%of the training data, our proposed methods can already achieve better performance compared to the baseline learned from the whole training set.¹

References

1. P. F. Brown, P. V. deSouza, R. L. Mercer, V. J. D. Pietra, and J. C. Lai. Class-based n-gram models of natural language. Computational Linguistics, pages 467--479, 1992.
2. P.-C. Chang, M. Galley, and C. D. Manning. Optimizing chinese word segmentation for machine translation performance. In Proceedings of the Third Workshop on Statistical Machine Translation, pages 224--232, June 2008.
3. Y. R. Chao. The efficiency of the chinese language. In Proc. the General Conference of UNESCO, 1946.
4. H.-H. Chen, S.-J. Huang, Y.-W. Ding, and S.-C. Tsai. Proper Name Translation in Cross-Language Information Retrieval. In Proc. ACL, 1998.
5. Y. Chen, C. Zong, and K.-Y. Su. On jointly recognizing and aligning bilingual named entities. In ACL, 2010.
6. Y. Deng and Y. Gao. Guiding Statistical Word Alignment Models With Prior Knowledge. In Proc. ACL, 2007.
7. D. Feng, Y. Lv, and M. Zhou. A new approach for english-chinese named entity alignment. In Proc. PACLIC, 2004.
8. U. Hermjakob, K. Knight, and H. D. III. Name translation in statistical machine translation: Learning when to transliterate. In Proc. ACL, 2008.
9. F. Huang and S. Vogel. Improved named entity translation and bilingual named entity extraction. In Proc. 2002 International Conference on Multimodal Interfaces, 2002.
10. H. Ji and R. Grishman. Analysis and repair of name tagger errors. In Proc. COLING-ACL, 2006.
11. H. Ji and R. Grishman. Collaborative entity extraction and translation. In Proc. RANLP, 2007.
12. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, pages 282--289, 2001.
13. R. C. Moore. Learning translations of named-entity phrases from parallel corpora. In Proc. EACL, 2003.
14. F. J. Och and H. Ney. Improved statistical alignment models. In ACL, 2000.
15. K. Parton and K. McKeown. Mt error detection for cross-lingual question answering. Proc. COLING2010, 2010.
16. M. Snover, X. Li, W.-P. Lin, Z. Chen, S. Tamang, M. Ge, A. Lee, Q. Li, H. Li, S. Anzaroot, and H. Ji. Cross-lingual slot filling from comparable corpora. In Proc. ACL2011 Worshop on Building and Using Comparable Corpora, 2011.
17. C. A. Sutton, A. McCallum, and K. Rohanimanesh. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. Journal of Machine Learning Research, 8:693--723, 2007.
18. K. Tsuji. Automatic extraction of translational japanese-katakana and english word pairs from bilingual corpora. 15(3), 2002.
19. A. K. McCallum. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu, 2002.
20. M. J. Wainwright, T. Jaakkola, and A. S. Willsky. Tree-based reparameterization for approximate inference on loopy graphs. In NIPS, pages 1001--1008, 2001.

Index Terms

• Information systems

  • Information systems applications