Abstract
Several information organization, access, and filtering systems can benefit from different kind of document representations than those used in traditional Information Retrieval (IR). Topic Detection and Tracking (TDT) is an example of such a domain. In this paper we demonstrate that traditional methods for term weighing does not capture topical information and this leads to inadequate representation of documents for TDT applications. We present various hypotheses regarding the factors that can help in improving the document representation for Story Link Detection (SLD) - a core task of TDT. These hypotheses are tested using various TDT corpora. From our experiments and analysis we found that in order to obtain a faithful representation of documents in TDT domain, we not only need to capture a term’s importance in traditional IR sense, but also evaluate its topical behavior. Along with defining this behavior, we propose a novel measure that captures a term’s importance at the corpus level as well as its discriminating power for topics. This new measure leads to a much better document representation as reflected by the significant improvements in the results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Salton, G.: Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer. Addison-Wesley, Reading (1989)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Research and Development in Information Retrieval, pp. 275–281 (1998)
Tomita, J., Hayashi, Y.: Improving Effectiveness and Efficiency of Web Search by Graph-based Text Representation. In: Proceedings of The Ninth World Wide Web Conference (2000)
Collins-Thompson, K., Callan, J.: Query expansion using random walk models. In: CIKM (2005)
Allan, J.: Topic Detection and Tracking. Kluwer Academic Publishers, Dordrecht (2002)
Allan, J., et al.: Topic-based novelty detection. Technical report, Center for Language and Speech Processing, John Hopkins University (1999)
Kumaran, G., Allan, J.: Text classification and named entities for New Event Detection. In: Proceedings of ACM SIGIR, pp. 297–304. ACM Press, New York (2004)
Shah, C., Croft, W.B., Jensen, D.: Representing Documents with Named Entities for Story Link Detection (SLD). In: CIKM (2006)
Eichmann, D.: Experiments with tracking / detection / etc. using entities and noun phrases. Technical report, University of Iowa (2001)
Fiscus, J., Wheatley, B.: Overview of the tdt 2004 evaluation and results. Technical report, NIST (2004)
Lavrenko, V., Croft, W.B.: Relevance-based language models. In: Proceedings of the 24th annual international ACM SIGIR conference, pp. 120–127. ACM Press, New York (2001)
Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the 10th International Conference on Information and Knowledge Management, pp. 403–410 (2001)
Fiscus, J.G., Doddington, G.R.: Topic detection and tracking evaluation overview. In: Allan, J. (ed.) Topic detection and tracking, pp. 17–32. Kluwer Academic Publishers, Dordrecht (2002)
Lavrenko, V., et al.: Relevance models for topic detection and tracking. In: Proceedings of Human Language Technologies Conference, pp. 104–110 (2002)
Lewis, D.D.: Evaluating and optmizing autonomous text classification systems. In: Fox, E.A., Ingwersen, P., Fidel, R. (eds.) Proceedings of SIGIR-95, 18th ACM International Conference on Research and Development in Information Retrieval, Seattle, US, pp. 246–254. ACM Press, New York (1995)
Kanungo, T., et al.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 881–892 (2002)
Tomokiyo, T., Hurst, M.: A Language Model Approach to Keyphrase Extraction. In: Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, pp. 33–40 (2003)
Kelly, D., et al.: A User-Centered Approach to Evaluating Topic Models. In: McDonald, S., Tait, J. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 27–41. Springer, Heidelberg (2004)
Gillick, L., Cox, S.: Some Statistical Issues in the Comparison of Speech Recognition Algorithms. In: Proceedings of IEEE’s ICASSP 1989, pp. 532–535. IEEE, Los Alamitos (1989)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Shah, C., Eguchi, K. (2007). Use of Topicality and Information Measures to Improve Document Representation for Story Link Detection. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-71496-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71494-1
Online ISBN: 978-3-540-71496-5
eBook Packages: Computer ScienceComputer Science (R0)