The DOI matches its Crossref record.
Extracting collective expectations about the future from large text collections
Abstract
News articles often contain information about the future. Given the huge volume of information available nowadays, an automatic way for extracting and summarizing future-related information is desirable. Such information will allow people to obtain a collective image of the future, to recognize possible future scenarios and be prepared for the future events. We propose a model-based clustering algorithm for detecting future events based on information extracted from a text corpus. The algorithm takes into account both textual and temporal similarity of sentences. We demonstrate that our algorithm can be used to discover future events and estimate their probabilities over time.
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In the citing paper
“This is shown in Fig. 8, where most of the detected temporal expressions occur in a two-year long time window with regard to the document timestamp and the near past and the near future tend to be referred to by fine granularity expressions, such as days.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Jatowt and Yeung 2011
In the citing paper
“This is shown in Fig. 8, where most of the detected temporal expressions occur in a two-year long time window with regard to the document timestamp and the near past and the near future tend to be referred to by fine granularity expressions, such as days.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Jatowt and Yeung 2011
In the citing paper
“[Jatowt and Yeung 2011] extended the above concepts by taking into account the uncertainty of a piece of future-related information.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Jatowt and Yeung 2011
In the citing paper
“[Jatowt and Yeung 2011] proposed a clustering algorithm to detect future events based on the information extracted from a reference text corpus.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Jatowt and Yeung 2011
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 10
- 10.1145/1645953.1645968 · DOI
- R. Baeza-Yates. Searching the Future. In MF/IR 2005 , 2005 . R. Baeza-Yates. Searching the Future. In MF/IR 2005, 2005.
- W. B. Cavnar and J. M. Trenkle . N-Gram-Based Text Categorization. In SDAIR '94 , 161--175, 1994 . W. B. Cavnar and J. M. Trenkle. N-Gram-Based Text Categorization. In SDAIR '94, 161--175, 1994.
- 10.1145/1555400.1555420 · DOI · Paper page
- 10.1109/WI-IAT.2011.250 · DOI
- 10.1145/2009916.2010018 · DOI · Paper page
- 10.3115/1075218.1075228 · DOI · Paper page
- M. Matthews Searching through Time in the New York Times . In HCIR '10 , 2010 . M. Matthews et al. Searching through Time in the New York Times. In HCIR '10, 2010.
- 10.1145/1183614.1183627 · DOI
- X. Zhu , Z. Ghahramani , and J. Lafferty . Time-sensitive Dirichlet Process Mixture Models. Technical report , Carnegie Mellon University , 2005 .. X. Zhu, Z. Ghahramani, and J. Lafferty. Time-sensitive Dirichlet Process Mixture Models. Technical report, Carnegie Mellon University, 2005..
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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
Self-citation
Metadata
- Authors
- Jatowt, A., & Au Yeung, C.-M.
- Affiliations
- Kyoto University, Kyoto, Japan
- ASTRI, Hong Kong, Hong Kong
- Venue
- Proceedings of the 20th ACM international conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2011-10-24
- Pages
- 1259-1264
- Cited by
- 28
- References
- 0
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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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