All issues

List of references:

1. E. Agichtein, V. Ganti. Mining reference tables for automatic text segmentation / Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. — ACM Press, 2004. — P. 20–29.
2. D. Beeferman, A. Berger, J. Lafferty. Statistical models for text segmentation / Machine Learning. — 1999. — P. 177–210.
3. M. Bilenko, R. Mooney, W. Cohen, P. Ravikumar, S. Fienberg. Adaptive name matching in information integration // IEEE Intelligent Systems. — 2003. — V. 18. — P. 16–23. — DOI: 10.1109/MIS.2003.1234765.
4. V. Borkar, K. Deshmukh, S. Sarawagi. Automatic segmentation of text into structured records. — 2001.
5. T. Brants. Topic-based document segmentation with probabilistic latent semantic analysis / Proceedings of CIKM. — ACM Press, 2002. — P. 211–218. — McLean. — ads: 2002evn..conf..203B.
6. F. Y. Y. Choi, P. Wiemer-Hastings, J. Moore. Latent semantic analysis for text segmentation / Proceedings of EMNLP. — 2001. — P. 109–117.
7. P. Christen. A survey of indexing techniques for scalable record linkage and deduplication // IEEE Transactions on Knowledge and Data Engineering. — 2012. — V. 24. — P. 1537–1555. — DOI: 10.1109/TKDE.2011.127.
8. P. Christen, T. Churches, J. X. Zhu. Probabilistic name and address cleaning and standardisation. — 2002.
9. W. W. Cohen, J. Richman. Learning to match and cluster large high-dimensional data sets for data integration. — 2002.
10. W. W. Cohen, P. Ravikumar, S. E. Fienberg. A comparison of string distance metrics for namematching tasks. — 2003. — P. 73–78.
11. E. Cortez, A. S. da Silva, M. A. Gon¸calves, F. Mesquita, E. S. de Moura. Flux-cim: flexible unsupervised extraction of citation metadata / JCDL ’07: Proceedings of the 7th ACM.
12. I. G. Councill, C. L. Giles, M. y. Kan. Parscit: An open-source crf reference string parsing package / International language resources and evaluation. — European Language Resources Association, 2008.
13. A. K. Elmagarmid, P. G. Ipeirotis, V. S. Verykios. Duplicate record detection: A survey / Transactions on knowledge and data engineering. — 2007. — P. 2007. — MathSciNet: MR2269091.
14. H. H. C. L. Giles, E. Manavoglu, H. Zha, Z. Zhang, E. A. Fox. Automatic document metadata extraction using support vector machines / JCDL ’03: Proceedings of the 3rd ACM.
15. T. Kudoh, Yu. Matsumoto. Use of support vector learning for chunk identification / Proceedings of CoNLL-2000 and LLL-2000. — 2000. — P. 142–144.
16. J. Lafferty. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. — Morgan Kaufmann, 2001. — P. 282–289.
17. A. McCallum, K. Nigam, L. H. Ungar. Efficient clustering of high-dimensional data sets with application to reference matching. — 2000.
18. J. R. Quinlan. C4.5: programs for machine learning. — San Francisco, CA, USA: Morgan Kaufmann Publishers Inc, 1993.
19. L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition // Proceedings of the IEEE. — 1989. — V. 77, no. 2. — P. 257–286. — DOI: 10.1109/5.18626.
20. M. Skounakis, M. Craven, S. Ray. Hierarchical hidden markov models for information extraction / Proceedings of the 18th International Joint Conference on Artificial Intelligence. — Morgan Kaufmann, 2003. — P. 427–433.
21. S.-Z. Yu. Hidden semi-markov models // Artificial Intelligence. — 2010. — MathSciNet: MR2724430.
22. T. Zhang, F. Damerau, D. Johnson. Text chunking based on a generalization of winnow // Journal of Machine Learning Research. — 2001. — no. 2. — P. 615–637.