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Linguistic Analysis and Extraction Tool for Online News Articles
Abstract
Information extraction has become an important technology to help users locate desired information on the Web. Designing a generalized method for extracting Web information is complicated due to the heterogeneity of Web information. Because of this, domain specific characteristics are often considered for effective Web information extracting. One such domain is on-line news websites. With thousands of new websites to provide daily news in today’s Web, it is critical to provide a tool that can automatically extract online news information for users. Most of previous approaches use manually or automatically constructed wrappers to extract news information. Several problems exist in previous approaches for online news extraction which requires a training stage to derive software. Extraction results may not be satisfactory when training set is too small. Second, even with these prerequisites satisfied, the extraction results may still be unstable and domain/site dependent. The motivation of our research is to identify and recognize news content, and provide an effective news extraction algorithm that is stable across any presentation designs and news domains.
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References
20 Newsgroups Dataset. http://kdd.ics.uci.edu/databases/20newsgroups/. 2008.
Vijayta patil and shishir shandilya J. 2011 Online news headline technique using weight updation tree.
Automatic Content Extraction. http://www.nist.gov/speech/tests/ace/2007/index.html. 2007
Lin, D., Pantel, P.: Discovery of Inference Rules for Question Answering. Natural Language Engineering. Volume 7-4. 2001.
Ji, H., Grishman, R.: Improving name tagging by reference resolution and relation detection. The 21st International Committee for Computational Linguistic and the 43rd Association for Computational Linguistics. 2002.
Jiang, J., Zhai. C. X.: A Systematic Exploration of the Feature Space for Relation Extraction. Proceedings of the Human Language Technologies and the Annual Conference of the North American Chapter of the Association for Computational Linguistics. 2007.
Hu, M., Liu, B.: Mining and summarizing customer reviews. Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining. 2004.
Reuters-21578 Dataset. http://kdd.ics.uci.edu/databases/reuters21578/. 2008
Fillmore, C. J., Narayanan, S., Baker, C. F.: What can linguistics contribute to event extraction? 21st Conference on Artificial Intelligence. Workshop on Event Extraction and Syndissertation. 2006.
Dave, K., Lawrence, S., Pennock, D. M.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. The 12th International World Wide Web Conference. 2003.
Holub, M., Böhmová, A.: Use of dependency tree structures for the microcontext extraction. Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics. Volume 11, Page 23-33. 2000.
Agrawal, R., Srikant, S.: Mining Sequential Patterns. The 11th International Conference on Data Engineering. 1994.
Furnkranz, J., Mitchell, T., Riloff, E.: A case study using linguistic phrases for text categorization on the WWW. AAAI-98 Workshop on Learning for Text Categorization. 1998.
Okanohara, D., Miyao, Y., Tsuruoka, Y., Tsujii, J.: Improving the Scalability of Semi-Markov Conditional Random Fields for Named Entity Recognition. The 21st International Committee for Computational Linguistic and the 44th annual meeting of the Association for Computational Linguistics. 2006.
Girju, R., Badulescu, A., Moldovan, D.: Automatic Discovery of Part-Whole Relations. Computational Linguistics. 32(1): 83-135. 2006.
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