Chao Chen
ABSTRACT
Quantities of valuable relation knowledge are contained in textual documents on the World Wide Web. However, those data are always organized in semi-structured text and cannot be used directly. We develop an automatic and effective approach to extract relations from World Wide Web, which just requires a few user specified seed instances as input. Those instances are used to generate extraction rules that in turn result in new instances. And in order to improve the reliability of results, an effective method is proposed to assess new extracted instances. This paper introduces the approach in details and the experimental results show that the approach achieves an average precision of 98.67% and can preferably complete the relation extraction task.
- Brin, S. 1998. "Extracting patterns and relations from the World Wide Web". In Proceedings of the 1998 International Work-shop on the Web and Databases (WebDB'98), March 1998. Google Scholar
Digital Library
- Freitag, D. and McCallum, A. 1999. "Information extraction with HMMs and shrinkage". In Proceedings of the AAAI-99 Workshop on Machine Learning for Information Extraction, 1999.Google ScholarDigital Library
- Skounakis, M., Craven, M. and Ray, S. 2003. "Hierarchical hidden markov models for information extraction". In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 2003. Google ScholarDigital Library
- Soderland, S. 1999. "Learning information extraction rules for semi-structured and free text". Machine Learning, 34(1-3):233--272, 1999. Google ScholarDigital Library
- McCallum, A. 2003. "Efficiently inducing features or conditional random fields". In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 2003. Google ScholarDigital Library
- Che, W. X., Liu, T., Li, S. 2005. "Automatic Entity Relation Extraction". Journal of Chinese Information Processing, 2005, 19(2):1--6.Google Scholar
- Wang, Y., Xu, D. Z. and Chen, J. E. 2009. "Entity Relation Extraction for Complex Chinese Text". Computer Science, Vol.36, No.8, 2009.Google Scholar
- Jiang, J. F. and Wang, S. X. 2005. "A Bootstrapping Method for Acquisition of Bi-relations and Bi-relations Patterns". Journal of Chinese Information Processing, 2005, 19(2): 71--77.Google Scholar
- Agichtein, E. and Gravano, L. 2000. "Snowball: Extracting Relations from Large Plain-Text Collections". Proceedings of the 5th ACM International Conference on Digital Libraries, June 2000. Google ScholarDigital Library
- Baidu, http://www.baidu.com.Google Scholar
- ICTCLAS, http://ictclas.org.Google Scholar
- Li, W. G., Liu, T. and Li, S. 2007. "Automated Entity Relation Tuple Extracting Using Web Mining". Acta Electronica Sinica, Vol. 35. No. 11, 2007.Google Scholar
Index Terms
- REV: extracting entity relations from world wide web
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