Abstract
Topic Detection and Tracking is an event-based information organization task where online news streams are monitored in order to spot new unreported events and link documents with previously detected events. The detection has proven to perform rather poorly with traditional information retrieval approaches. We present an approach that formalizes temporal expressions and augments spatial terms with ontological information and uses this data in the detection. In addition, instead using a single term vector as a document representation, we split the terms into four semantic classes and process and weigh the classes separately. The approach is motivated by experiments.
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
Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report. In: Proc. DARPA Broadcast News Transcription and Understanding Workshop. (1998)
Yang, Y., Carbonell, J., Brown, R., Pierce, T., Archibald, B. T., Liu, X.: Learning approaches for detecting and tracking news events. IEEE Intelligent Systems Special Issue on Applications of Intelligent Information Retrieval 14 (1999) 32–43
Allan, J., ed.: Topic Detection and Tracking-Event — based Information Organization. Kluwer Academic Publishers (2002)
Allan, J., Lavrenko, V., Jin, H.: First story detection in TDT is hard. In: Proc. 9th Conference on Information Knowledge Management CIKM, McClean, VA USA (2000) 374–381
Papka, R., Allan, J.: On-line new event detection using single-pass clustering. Technical Report IR-123, Department of Computer Science, University of Massachusetts (1998)
Allan, J., Lavrenko, V., Papka, R.: Event tracking. Technical Report IR-128, Department of Computer Science, University of Massachusetts (1998)
Lavrenko, V., Allan, J., DeGuzman, E., LaFlamme, D., Pollard, V., Thomas, S.: Relevance models for topic detection and tracking. In: Proc. Human Language Technology Conference (HLT). (2002)
Makkonen, J., Ahonen-Myka, H., Salmenkivi, M.: Applying semantic classes in event detection and tracking. In: Proc. International Conference on Natural Language Processing (ICON’02), Mumbai, India (2002)
van Mulbregt, P., Carp, I., Gillick, L., Lowe, S., Yamron, J.: Text segmentation and topic tracking on broadcast news via a hidden markov model approach. In: Proc. 5th Intl. Conference on Spoken Language Processing (ICSLP’98). (1998)
Yang, Y., Ault, T., Pierce, T., Lattimer, C.: Improving text categorization methods for event detection. In: Proc. ACM SIGIR. (2000) 65–72
Seymore, K., Rosenfeld, R.: Large-scale topic detection and language model adaptation. Technical report, School of Computer Science, Carnegie Mellon University (1997)
Baker, L. D., Hofmann, T., McCallum, A., Yang, Y.: A hierarchical probabilistic model for novelty detection in text. unpublished manuscript (1999)
Allan, J., Jin, H., Rajman, M., Wayne, C., Gildea, D., Lavrenko, V., Hoberman, R., Caputo, D.: Topic-based novelty detection. Technical Report Summer Workshop Final Report, Center for Language and Speech Processing, Johns Hopkins University (1999)
Yang, Y., Zhang, J., Carbonell, J., Jin, C.: Topic-conditioned novelty detection. In: Proc. ACM SIGKDD (to appear), Edmonton, Canada (2002)
Schilder, F., Habel, C.: From temporal expressions to temporal information: Semantic tagging of news messages. In: Proc. ACL-2001 Workshop on Temporal and Spatial Information Processing. (2001) 65–72
Goralwalla, I. A., Leontiev, Y., Özsu, M. T., Szafron, D., Combi, C.: Temporal granularity: Completing the puzzle. Journal of Intelligent Information Systems 16 (2001) 41–63
Makkonen, J., Ahonen-Myka, H.: Extraction and comparison of temporal evidence in identifying news events. Unpublished manuscript
Krippendorff, K.: On the reliability of unitizing continuous data. In Marsden, P. V., ed.: Sociological Methodology. Blackwell (1995) 47–76
Galton, A.: Time and change for AI. In Gabbay, M., Hogger, C. J., Robinson, J. A., eds.: Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 4, Epistemic and Temporal Reasoning. Oxford University Press (1995) 175–240
van Rijsbergen, C. J.: Information Retrieval. 2nd edn. Butterworths (1980)
Lee, L.: On the effectiveness of the skew divergence for statistical language analysis. In: Artificial Intelligence and Statistics. (2001) 65–72
Allan, J., Lavrenko, V., Swan, R.: Explorations within topic tracking and detection. In Allan, J., ed.: Topic Detection and Tracking — Event-based Information Organization. Kluwer Academic Publisher (2002) 197–224
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Makkonen, J., Ahonen-Myka, H., Salmenkivi, M. (2003). Topic Detection and Tracking with Spatio-Temporal Evidence. In: Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2003. Lecture Notes in Computer Science, vol 2633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36618-0_18
Download citation
DOI: https://doi.org/10.1007/3-540-36618-0_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-01274-0
Online ISBN: 978-3-540-36618-8
eBook Packages: Springer Book Archive