Abstract
Topic Link Detection aims to detect whether a pair of random stories discuss the same topic, which is an important subtask of Topic Detection and Tracking. In previous works, statistical method and machine-learning approach are used more often than not, however, the semantic distribution of a story and the structure relationship of contents are ignored. A new method based on the semantic domain is proposed for the purpose of improved the precision. In this method, every story is divided some semantic domain through analyzing internal semantic distribution and structure relationships of contexts. The results of experiment proved that the proposed method can improve performance of system.
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References
The 2004 Topic Detection and Tracking (TDT 2004) Task Definition and Evaluation Plan (EB/OL) (2004), http://www.nist.gov
Kumaran, G., Allan, J.: Text classification and named entities for new event detection. In: Proceeding of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 297–304. ACM, New York (2004)
Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yiming, Y.: Topic detection and tracking pilot study final report. In: Proceedings of the Broadcast News Transcription and Understanding Workshop, vol. 2, pp. 1–25 (1998)
Naptali, W., Tsuchiya, M., Nakagawa, S.: Topic-dependent language model with voting on noun history. ACM Transactions on Asian Language Information Processing(TALIP) 9(7) (2010)
Ponte, J.M., Bruce Croft, W.: A language modeling approach to information retrieval. In: Proc. SIGIR, pp. 275–281 (1998)
Ha-Thuc, V., Srinivasan, P.: Topic models and a revisit of text-related applications. In: Proceedings of the 2nd PHD Workshop on Information and Knowledge Management, pp. 25–32 (2008)
Nallapati, R., Feng, A., Peng, F., Allan, J.: Event threading within news topics. In: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management (CIKM), pp. 446–453 (2004)
Chaitanya, C., Smyth, P., Steyvers, M.: Combining Concept Hierarchies and Statistical Topic Models. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 1469–1470 (2008)
Hong, Y., Zhang, Y., Fan, J.L., Liu, T., Sheng, L.: Chinese topic link detection based on semantic domain language model. Journal of Software 19(9), 2265–2275 (2008)
Wang, L., Li, F.: Story Link Detection Based on Event Words. In: Gelbukh, A. (ed.) CICLing 2011, Part II. LNCS, vol. 6609, pp. 202–211. Springer, Heidelberg (2011)
Shah, C., Eguchi, K.: Improving document representation for story link detection by modeling term topicality. Information and Media Technologies 4(2), 433–441 (2009)
Hong, Y., Zhang, Y., Fan, J.L., Liu, T., Li, S.: New Event Detection Based on Division Comparison of Subtopic. Chinese Journal of Computers 31(4), 687–695 (2008)
Lakshmi, K., Mukherjee, S.: Using Cohesion-model for story link detection system. Intenational Journal of Computer Science and Network Security 7(3), 59–66 (2007)
Zhang, K., Zi, J., Wu, L.G.: New event detection based on indexing-tree and named entity. In: Proceedings of the 30th Annual Int’1 ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 215–222 (2007)
Nomoto, T.: Two-tier Similarity Model for Story Link Detection. In: Proceedings of the 19th ACM International Conference Information and Knowledge Management, pp. 789–798 (2010)
Zhang, K., Li, J.Z., Wu, G., Wang, K.H.: Term-Committee-Based Event Identification Within Topics. Journal of Computer Research and Development 46(2), 245–252 (2009)
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Liu, PY., Yang, YZ., Fei, SD., Zhang, Z. (2014). Research on Topic Link Detection Method Based on Semantic Domain. In: Zu, Q., Vargas-Vera, M., Hu, B. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2013. Lecture Notes in Computer Science, vol 8351. Springer, Cham. https://doi.org/10.1007/978-3-319-09265-2_33
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DOI: https://doi.org/10.1007/978-3-319-09265-2_33
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