Information extraction: Methodologies and applications

Emerging Technologies of Text Mining: Techniques and Applications

Volumn , Issue , 2007, Pages 1-33

  Tang, Jie ^a   Hong, Mingcai ^a   Zhang, Duo ^a   Liang, Bangyong ^b   Li, Juanzi ^a  

^a TSINGHUA UNIVERSITY   (China)

^b NEC CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 49749096520     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59904-373-9.ch001     Document Type: Chapter

References (70)

1. Abney, S. (1991). Parsing by chunks. In R. Berwick, S. Abney, & C. Tenny (Eds.), Principle-based parsing: Computation and psycholinguistics (pp. 257-278). Boston: Kluwer Academic Publishers.
2. Berger, A. L., Della Pietra, S. A., & Della Pietra, V. J. (1996). A maximum entropy approach to natural language processing. Computational Linguistics, 22, 39-71.
3. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In D. Haussler (Ed.), 5th Annual ACM Workshop on COLT (pp. 144-152). Pittsburgh, PA: ACM Press.
4. Bunescu, R., Ge, R., Kate, R. J., Marcotte, E. M., Mooney, R. J., Ramani, A. K., et al. (2005). Comparative experiments on learning information extractors for proteins and their interactions. Artificial Intelligence in Medicine [special issue on Summarization and Information Extraction from Medical Documents], 33(2), 139-155.
5. Bunescu, R., & Mooney, R. J. (2005). Statistical relational learning for natural language information extraction. In L. Getoor & B. Taskar (Eds.), Statistical relational learning, forthcoming.
6. Califf, M. E., & Mooney, R. J. (1998). Relational learning of pattern-match rules for information extraction. In Working Notes of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing (pp. 6-11).
7. Califf, M. E., & Mooney, R. J. (2003). Bottomup relational learning of pattern matching rules for information extraction. Journal of Machine Learning Research, 4, 177-210.
8. Chen, S. F., & Rosenfeld, R. (1999). A Gaussian prior for smoothing maximum entropy models (Tech. Rep. No. CMU-CS-99-108). Carnegie Mellon University.
9. Ciravegna, F. (2001). (LP)2 An adaptive algorithm for information extraction from Web-related texts. In Proceedings of the IJCAI-2001 Workshop on Adaptive Text Extraction and Mining held in conjunction with 17th International Joint Conference on Artificial Intelligence (IJCAI), Seattle, Washington.
10. Collins, M. (2002). Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proceedings of the Conference on Empirical Methods in NLP (EMNLP'02) (pp. 1-8).
11. Craven, M., & Kumlien, J. (1999). Constructing biological knowledge bases by extracting information from text sources. In Proceedings of the 7th International Conference on Intelligent Systems for Molecular Biology (ISMB-1999) (pp. 77-86).
12. Darroch, J. N., & Ratcliff, D. (1972). Generalized iterative scaling for log-linear models. The Annals of Mathematical Statistics, 43(5), 1470-1480.
13. Ducheneaut, N., & Bellotti, V. (2001). E-mail as habitat: An exploration of embedded personal information management. Interactions, 8, 30-38.
14. Finkel, J. R., Grenager, T., & Manning, C. D. (2005). Incorporating nonlocal information into information extraction systems by Gibbs sampling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL-2005) (pp. 363-370).
15. Finn, A., & Kushmerick, N. (2004). Information extraction by convergent boundary classification. In AAAI-04 Workshop on Adaptive Text Extraction and Mining (pp. 1-6). San Jose, CA.
16. Finn, A. (2006). A multi-level boundary classification approach to information extraction. Doctoral thesis, University College Dublin.
17. Freitag, D. (1998). Information extraction from HTML: Application of a general machine learning approach. In Proceedings of the 15th Conference on Artificial Intelligence (AAAI'98) (pp. 517-523).
18. Freitag, D., & Kushmerick, N. (2000). Boosted wrapper induction. In Proceedings of 17th National Conference on Artificial Intelligence (pp. 577-583).
19. Ghahramani, Z., & Jordan, M. I. (1997). Factorial hidden Markov models. Machine Learning, 29, 245-273.
20. Hammersley, J., & Clifford, P. (1971). Markov fields on finite graphs and lattices. Unpublished manuscript.
21. Han, H., Giles, L., Manavoglu, E., Zha, H., Zhang, Z., & Fox, E.A. (2003). Automatic document metadata extraction using support vector machines. In Proceedings of 2003 Joint Conference on Digital Libraries (JCDL'03) (pp. 37-48).
22. Kauchak, D., Smarr, J., & Elkan, C. (2004). Sources of success for boosted wrapper induction. The Journal of Machine Learning Research, 5, 499-527.
23. Kristjansson, T. T., Culotta, A., Viola, P. A., & McCallum, A. (2004). Interactive information extraction with constrained conditional random fields. In Proceedings of AAAI'04 (pp. 412-418).
24. Kushmerick, N., Weld, D. S., & Doorenbos, R. (1997). Wrapper induction for information extraction. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'97) (pp. 729-737).
25. Kushmerick, N. (2000). Wrapper induction: Efficiency and expressiveness. Artificial Intelligence, 118, 15-68.
26. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning (ICML'01) (pp. 282-289).
27. Lawrence, S., Giles, C. L., & Bollacker K. (1999). Digital libraries and autonomous citation indexing. IEEE Computer, 32(6), 67-71.
28. Li, X., & Liu, B. (2003). Learning to classify texts using positive and unlabeled data. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI'2003) (pp. 587-592).
29. McCallum, A., Freitag, D., & Pereira, F. (2000). Maximum entropy Markov models for information extraction and segmentation. In Proceedings of the 17th International Conference on Machine Learning (ICML'00) (pp. 591-598).
30. McCallum, A. (2003). Efficiently inducing features of conditional random fields. In Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence (pp. 403-410).
31. Morik, K., Brockhausen, P., & Joachims, T. (1999). Combining statistical learning with a knowledgebased approach: A case study in intensive care monitoring. In Proceedings of International Conference on Machine Learning (ICML'99) (pp. 268-277).
32. Muslea, I., Minton, S., & Knoblock, C. (1998). STALKER: Learning extraction rules for semistructured, Web-based information sources. In the AAAI Workshop on AI and Information Integration (pp. 74-81).
33. Muslea, I., Minton, S., & Knoblock, C. (1999). Hierarchical wrapper induction for semistructured information sources. Autonomous Agents and Multi-Agent Systems, 4, 93-114.
34. Muslea, I. (1999). Extraction patterns for information extraction tasks: A survey. In Proceedings of AAAI-99: Workshop on Machine Learning for Information Extraction.
35. Muslea, I., Minton, S., & Knoblock, C. A. (2003). Active learning with strong and weak views: A case study on wrapper induction. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (pp. 415-420).
36. Ng, H. T., Lim, C. Y., & Koo, J. L. T. (1999). Learning to recognize tables in free text. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics (ACL'99) (pp. 443-450).
37. Nocedal, J., & Wright, S. J. (1999). Numerical optimization. New York: Springer Press.
38. Peng, F. (2001). Models for information extraction (Technique Report).
39. Peng, F., & McCallum, A. (2004). Accurate information extraction from research papers using conditional random fields. In Proceedings of HLT-NAACL (pp. 329-336).
40. Pinto, D., McCallum, A., Wei, X., & Croft, W. B. (2003). Table extraction using conditional random fields. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'03) (pp. 235-242).
41. Ramshaw, L. A., & Marcus, M. P. (1995). Text chunking using transformation-based learning. In Proceedings of Third Workshop on Very Large Corpora, ACL (pp. 67-73).
42. Ratnaparkhi, A. (1998). Unsupervised statistical models for prepositional phrase attachment. In Proceedings of COLING ACL'98 (pp. 1079-1085). Montreal, Canada.
43. Riloff, E. (1993). Automatically constructing a dictionary for information extraction tasks. In Proceedings of the Eleventh National Conference on Artificial Intelligence (pp. 811-816).
44. Riloff, E. (1996). Automatically generating extraction patterns from untagged text. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 1044-1049).
45. Riloff, E., & Jones, R. (1999). Learning dictionaries for information extraction by multi-level bootstrapping. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (pp. 474-479).
46. Roth, D., & Yih, W. (2004). A linear programming formulation for global inference in natural language tasks. In Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) (pp. 1-8). Boston.
47. Schölkopf B., Burges, C. J. C., & Smola A. J. (1999). Advances in kernel methods: Support vector learning. MA: MIT Press.
48. Sha, F., & Pereira, F. (2003). Shallow parsing with conditional random fields. In Proceedings of Human Language Technology, NAACL (pp. 188-191).
49. Shapire, R. E. (1999). A brief introduction to boosting. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-1999) (pp. 1401-1405).
50. Shewchuk, J. R. (1994). An introduction to the conjugate gradient method without the agonizing pain. Retrieved March 22, 2007, from http://www-2.cs.cmu.edu/.jrs/jrspapers.html#cg
51. Siefkes, C., & Siniakov, P. (2005). An overview and classification of adaptive approaches to information extraction. Journal on Data Semantics, 4, 172-212.
52. Soderland, S., Fisher, D., Aseltine, J., & Lehnert, W. (1995). CRYSTAL: Inducing a conceptual dictionary. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI'95) (pp. 1314-1319).
53. Soderland, S. (1999). Learning information extraction rules for semi-structured and free text. Machine Learning, 34(1-3), 299-272.
54. Sutton, C., Rohanimanesh, K., & McCallum, A. (2004). Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. In Proceedings of ICML'2004 (pp. 783-790).
55. Sutton, C., & McCallum, A. (2005). An introduction to conditional random fields for relational learning. In L. Getoor & B. Taskar (Eds.), Statistical relational learning, forthcoming.
56. Tang. J., Li, H., Cao, Y., & Tang, Z. (2005). E-mail data cleaning. In Proceedings of SIGKDD'2005 (pp. 489-499). Chicago.
57. Tang. J., Li, J., Lu, H., Liang, B., & Wang, K. (2005). iASA: Learning to annotate the semantic Web. Journal on Data Semantic, 4, 110-145.
58. Tang. J., Hong, M., Zhang, J., Liang, B., & Li, J. (2006). A new approach to personal network search based on information extraction. In Proceedings of the first International Conference of Asian Semantic Web (ASWC). To appear.
59. Tang. J., Hong, M., Li, J., & Liang, B. (2006). Treestructured conditional random fields for semantic annotation. In Proceedings of 5th International Conference of Semantic Web (ISWC'2006) (pp. 640-653).
60. Taskar, B., Guestrin, C., & Koller, D. (2003). Max-margin markov networks. In Proceedings of Advances in Neural Information Processing Systems (NIPS'03). Cambridge, MA: MIT Press.
61. Tetko, I.V., Livingstone, D.J., & Luik, A.I. (1995). Neural network studies. 1. Comparison of overfitting and overtraining. Journal of Chemical Information and Computer Sciences, 35, 826-833.
62. Tjong Kim Sang, E. F., & Buchholz, S. (2000). Introduction to the CoNLL-2000 shared task: Chunking. In Proceedings of CoNLL-2000 (pp. 127-132).
63. Vapnik, V. (1998). Statistical learning theory. NY: Springer-Verlag.
64. Vapnik V. (1999). The nature of statistical learning theory. NY: Springer-Verlag.
65. Wallach, H. (2002). Efficient training of conditional random fields. Unpublished master's thesis, University of Edinburgh, USA.
66. Wang, Y., & Hu, J. (2002). A machine learning based approach for table detection on the Web. In Proceedings of the 11th International World Wide Web Conference (WWW'02) (pp. 242-250). Honolulu, Hawaii.
67. Wainwright, M., Jaakkola, T., & Willsky, A. (2001). Tree-based reparameterization for approximate estimation on graphs with cycles. In Proceedings of Advances in Neural Information Processing Systems (NIPS'2001) (pp. 1001-1008).
68. Zelenko, D., Aone, C., & Richardella, A. (2003). Kernel methods for relation extraction. Journal of Machine Learning Research, 3, 1083-1106.
69. Zhang, L., Pan, Y., & Zhang, T. (2004). Recognizing and using named entities: Focused named entity recognition using machine learning. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'04) (pp. 281-288).
70. Zhu, J., Nie, Z., Wen, J., Zhang, B., & Ma, W. (2005). 2D conditional random fields for Web information extraction. In Proceedings of 22nd International Conference on Machine Learning (ICML2005) (pp. 1044-1051). Bonn, Germany.