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Cited by 35

The DOI matches its Crossref record.

Improving Temporal Language Models for Determining Time of Non-timestamped Documents

Kanhabua, Nattiya, Nørvåg, Kjetil·2008·Lecture Notes in Computer Science
DOI 10.1007/978-3-540-87599-4_37
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Overview

Integrity signals

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Crossref · 10.1007/978-3-540-87599-4_37

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Recomputing p-values needs the paper’s full text.

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Claims the paper makes

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Jong et al. 2005 and Kanhabua and Nørvåg 2008 approached the problem by determining the time of non-timestamped documents using temporal language models.

    “[Jong et al. 2005] and [Kanhabua and Nørvåg 2008] approached this problem by determining the time of nontimestamped documents, namely, news articles, resorting to temporal language models”

    From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Kanhabua and Nørvåg 2008

  • The process of determining a document's time dimension can be divided into two categories: determining a document's timestamp and determining its focus time.

    “this process can be divided into two categories: (1) determining a document's timestamp, i.e., the time when the document was created, published or lastmodified and (2) determining its focus time, i.e., the time to which its contents refer.”

    From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Kanhabua and Nørvåg 2008

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 17

  1. Alonso, O., Gertz, M.: Clustering of search results using temporal attributes. In: Proceeding of the 29th SIGIR (2006) · DOI · Paper page
  2. Berberich, K., Bedathur, S.J., Neumann, T., Weikum, G.: A time machine for text search. In: Proceedings of SIGIR 2007 (2007) · DOI
  3. de Jong, F., Rode, H., Hiemstra, D.: Temporal language models for the disclosure of historical text. In: Proceedings of AHC 2005 (History and Computing) (2005)
  4. Google Zeitgeist, http://www.google.com/press/zeitgeist.html
  5. Kraaij, W.: Variations on language modeling for information retrieval. SIGIR Forum 39(1), 61 (2005) · DOI
  6. Li, X., Croft, W.B.: Time-based language models. In: Proceedings of CIKM 2003 (2003) · DOI · Paper page
  7. Llidó, D.M., Llavori, R.B., Cabo, M.J.A.: Extracting temporal references to assign document event-time periods. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113. Springer, Heidelberg (2001) · DOI
  8. Lochbaum, K.E., Streeter, L.A.: Comparing and combining the effectiveness of latent semantic indexing and the ordinary vector space model for information retrieval. Inf. Process. Manage. 25(6), 665–676 (1989) · DOI
  9. Mani, I., Wilson, G.: Robust temporal processing of news. In: ACL 2000: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics (2000) · DOI · Paper page
  10. Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
  11. Nørvåg, K.: The design, implementation, and performance of the V2 temporal document database system. Journal of Information and Software Technology 46(9), 557–574 (2004) · DOI
  12. Nørvåg, K.: Supporting temporal text-containment queries in temporal document databases. Journal of Data & Knowledge Engineering 49(1), 105–125 (2004) · DOI
  13. Omar Alonso, M.G., Baeza-Yates, R.: On the value of temporal information in information retrieval. ACM SIGIR Forum 41(2), 35–41 (2007) · DOI
  14. Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of SIGIR 1998 (1998) · DOI · Paper page
  15. Swan, R., Allan, J.: Extracting significant time varying features from text. In: Proceedings of CIKM 1999 (1999) · DOI
  16. Swan, R., Jensen, D.: Timemines: Constructing timelines with statistical models of word usage. In: Proceedings of KDD-2000 Workshop on Text Mining (2000)
  17. Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22(2), 179–214 (2004) · DOI

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Metadata

Authors
Kanhabua, N., & Nørvåg, K.
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2008
Pages
358-370
ISSN
  • 0302-9743
  • 1611-3349
DOI
10.1007/978-3-540-87599-4_37
Cited by
35
References
0
Language
en
Licence

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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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