Abstract
The major challenging issue to determine the relevance and the novelty of sentences is the amount of information used in similarity computation among sentences. An information retrieval (IR) with reference corpus approach is proposed. A sentence is considered as a query to a reference corpus, and similarity is measured in terms of the weighting vectors of document lists ranked by IR systems. Two sentences are regarded as similar if they are related to the similar document lists returned by IR systems. A dynamic threshold setting method is presented. Besides IR with reference corpus, we also use IR systems to retrieve sentences from given sentences. The corpus-based approach with dynamic thresholds outperforms direct retrieval approach. The average F-measure of relevance and novelty detection using Okapi system was 0.212 and 0.207, 57.14% and 58.64% of human performance, respectively.
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References
Allan, J., Wade, C., Bolivar, A.: Retrieval and Novelty Detection at the Sentence Level. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, July 28-August 01, pp. 314–321. ACM, New York (2003)
Allan, J., Carbonnell, J., Yamron, J.: Topic Detection and Tracking: Event-Based Information Organization. Kluwer, Dordrecht (2002)
Chen, H.H., Ku, L.W.: An NLP & IR Approach to Topic Detection. In: Allan, J., Carbonnell, J., Yamron, J. (eds.) Topic Detection and Tracking: Event-Based Information Organization, pp. 243–264. Kluwer, Dordrecht (2002)
Chen, H.H., Kuo, J.J., Huang, S.J., Lin, C.J., Wung, H.-C.: A Summarization System for Chinese News from Multiple Sources. Journal of American Society for Information Science and Technology (2003)
Harman, D.: Overview of the TREC 2002 Novelty Trec. In: Proceedings of the Eleventh Text REtrieval Conference, November 19-22. TREC, NIST Special Publication, Gaithersburg (2002)
Larkey, L.S., et al.: UMass at TREC2002: Cross Language and Novelty Tracks. In: Proceedings of the Eleventh Text REtrieval Conference, November 19-22. TREC, NIST Special Publication, Gaithersburg (2002)
Robertson, S.E., Walker, S., Beaulieu, M.: Okapi at TREC-7: Automatic ad hoc, Filtering, VLC and Interactive. In: Proceedings of the Seventh Text REtrieval Conference, November 9-11. TREC 7, pp. 253–264. NIST Special Publication, Gaithersburg (1998)
Tsai, M.F., Chen, H.H.: Some Similarity Computation Methods in Novelty Detection. In: Proceedings of the Eleventh Text REtrieval Conference, November 19-22. TREC, NIST Special Publication, Gaithersburg (2002)
Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 5(24), 513–523
Voorhees, E.M., Harman, D.K. (eds.): Proceedings of the Sixth Text Retrieval Conference, November 19-21. NIST Special Publication, Gaithersburg (1997)
Zhang, M., et al.: THU at TREC2002: Novelty, Web and Filtering. In: Proceedings of the Eleventh Text REtrieval Conference, November 19-22. TREC, NIST Special Publication, Gaithersburg (2002)
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Chen, HH., Tsai, MF., Hsu, MH. (2004). Identification of Relevant and Novel Sentences Using Reference Corpus. In: McDonald, S., Tait, J. (eds) Advances in Information Retrieval. ECIR 2004. Lecture Notes in Computer Science, vol 2997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24752-4_7
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DOI: https://doi.org/10.1007/978-3-540-24752-4_7
Publisher Name: Springer, Berlin, Heidelberg
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