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Ranking related news predictions
Abstract
We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
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Kanhabua et al. 2011 proposed a ranking model for future predictions using a learning-to-rank algorithm.
“[Kanhabua et al. 2011] also proposed a ranking model for future predictions using a learning-to-rank algorithm”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Kanhabua et al. 2011
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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
Metadata
- Authors
- Kanhabua, N., Blanco, R., & Matthews, M.
- Affiliations
- Norwegian University of Science and Technology, Trondheim, Norway
- Yahoo! Research, Barcelona, Spain
- Venue
- Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
- Publisher
- ACM
- Published
- 2011-07-24
- Pages
- 755-764
- Cited by
- 24
- References
- 0
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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