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Text classification and named entities for new event detection
Abstract
New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.
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“[Kumaran and Allan 2004], on the other hand, detected new stories by measuring the degree overlap of one story with those that occurred in the past.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Kumaran and Allan 2004
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Research works printed but not linked: 10
- Detection In Topic, 2002, Kluwer Academic Publishers
- Allan J., 1999, Caputo Summer workshop final report. In Center for Language and Speech Processing
- 10.1145/354756.354843 · DOI
- 10.1023/A:1007558221122 · DOI
- 10.1145/860435.860495 · DOI
- 10.1007/978-3-7091-7557-6_14 · DOI
- 10.1145/160688.160718 · DOI
- 10.1023/A:1007649029923 · DOI
- 10.3115/1072133.1072182 · DOI
- 10.1145/775047.775150 · DOI
Cited by
Sorted by citations
Survey of Temporal Information Retrieval and Related Applications
Campos, R., Dias, G., Jorge, A. M., & Jatowt, A. (2015) · ACM Computing Surveys · doi:10.1145/2619088 · Cited by 262
SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
Arnold, S., Schneider, R., Cudré-Mauroux, P., Gers, F. A., & Löser, A. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00261 · Cited by 85
Metadata
- Authors
- Kumaran, G., & Allan, J.
- Affiliations
- University of Massachusetts Amherst, Amherst, MA
- Venue
- Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2004-07-25
- Pages
- 297-304
- Cited by
- 220
- References
- 0
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