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Retrieval and novelty detection at the sentence level
Abstract
Previous research in novelty detection has focused on the task of finding novel material, given a set or stream of documents on a certain topic. This study investigates the more difficult two-part task defined by the TREC 2002 novelty track: given a topic and a group of documents relevant to that topic, 1) find the relevant sentences from the documents, and 2) find the novel sentences from the collection of relevant sentences. Our research shows that the former step appears to be the more difficult part of this task, and that the performance of novelty measures is very sensitive to the presence of non-relevant sentences.
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Cited by
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Gurrin, C., Smeaton, A. F., & Doherty, A. R. (2014) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000033 · Cited by 434
Practical elimination of near-duplicates from web video search
Wu, X., Hauptmann, A. G., & Ngo, C.-W. (2007) · Proceedings of the 15th ACM international conference on Multimedia · doi:10.1145/1291233.1291280 · Cited by 340
SEA: Segment-enrich-annotate paradigm for adapting dialog-based content for improved accessibility
Candan, K. S., Dönderler, M. E., Hedgpeth, T., Kim, J. W., Li, Q., & Sapino, M. L. (2009) · ACM Transactions on Information Systems · doi:10.1145/1508850.1508853 · Cited by 8
Metadata
- Authors
- Allan, J., Wade, C., & Bolivar, A.
- Venue
- Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
- Publisher
- ACM
- Published
- 2003-07-28
- Pages
- 314-321
- OpenAlex
- W2072284402
- Cited by
- 278
- References
- 0
- FWCI
- 12.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Retrieval and novelty detection at the sentence level. https://app.cochat.ai/papers/10.1145/860435.860493
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- Allan, J., Wade, C., & Bolivar, A. (2003). Retrieval and novelty detection at the sentence level. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. https://doi.org/10.1145/860435.860493
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
