CoChat
Sign Up
Cited by 22

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

ChronoSeeker: search engine for future and past events

Kawai, Hideki, Jatowt, Adam, Tanaka, Katsumi, Kunieda, Kazuo, Yamada, Keiji·2010·Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
DOI 10.1145/2108616.2108647
Open this paper in CoChat — free account

Abstract

In this paper, we propose an on-demand search engine called ChronoSeeker, which allows users to find past/future events based on their interest. Our goal is providing a search engine which can collect as many future/past events as possible relevant to user's query in obtaining various future scenarios considering both predictions and histories. Two technical issues are treated, (1) efficient search method for event information and (2) accurate filtering method for removing noises from search results. To search for event information effectively, our system expands a user query by some typical expressions related to event information such as year expressions, temporal modifiers and context terms. To remove noisy information, we selected five types of features for a machine learning technique to classify candidates into event information or not. Our experiment showed that filtering performance achieved an 85% F-measure, and that query expansion can collect dozens of times more CEs than those without expansion.

Overview

Integrity signals

Registry

Crossref · 10.1145/2108616.2108647

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Flagged first, then by citations

Show all 1 references

Research works printed but not linked: 17

  1. R. Baeza-Yates . Searching the Future , ACM SIGIR Workshop MF/IR , 2005 . R. Baeza-Yates. Searching the Future, ACM SIGIR Workshop MF/IR, 2005.
  2. 10.1109/WI-IAT.2009.95 · DOI
  3. 10.1145/1379092.1379106 · DOI
  4. 10.1145/1081870.1081883 · DOI
  5. 10.1109/ISUC.2008.38 · DOI
  6. 10.1145/1555400.1555420 · DOI · Paper page
  7. S. Kim , H. Alani , W. Hall , P. H. Lewis , D. E. Millard , N. R. Shadbolt , M. J. Weal . Artequakt. Generating Tailored Biographies with Automatically Annotated Fragments from the Web , Semantic Authoring. Annotation and Knowledge Markup Workshop in the 15th European Conference on Artificial Intelligence , 2002 . S. Kim, H. Alani, W. Hall, P. H. Lewis, D. E. Millard, N. R. Shadbolt, M. J. Weal. Artequakt. Generating Tailored Biographies with Automatically Annotated Fragments from the Web, Semantic Authoring. Annotation and Knowledge Markup Workshop in the 15th European Conference on Artificial Intelligence, 2002.
  8. R. Kimura , S. Oyama , K. Tanaka . Automatic Collection of Personal Histories for Generating Who's Who from the Web. (in Japanese) , DBSJ Letters , 5 ( 2 ), 2006 . R. Kimura, S. Oyama, K. Tanaka. Automatic Collection of Personal Histories for Generating Who's Who from the Web. (in Japanese), DBSJ Letters, 5(2), 2006.
  9. 10.1145/1277741.1277845 · DOI
  10. 10.1145/1017068.1017069 · DOI
  11. G. Mishne , N. Glance . Predicting Movie Sales from Blogger Sentiment . Proceedings of the Spring Symposia on Computational Approaches to Analyzing Weblogs , 2006 . G. Mishne, N. Glance. Predicting Movie Sales from Blogger Sentiment. Proceedings of the Spring Symposia on Computational Approaches to Analyzing Weblogs, 2006.
  12. M. Pasca , D. Lin , J. Bigham , A. Lifchits , A. Jain . Organizing and Searching the World Wide Web of Facts - Step One: the One-Million Fact Extraction Challenge. The 21st National Conference on Artificial Intelligence , 2006 . M. Pasca, D. Lin, J. Bigham, A. Lifchits, A. Jain. Organizing and Searching the World Wide Web of Facts - Step One: the One-Million Fact Extraction Challenge. The 21st National Conference on Artificial Intelligence, 2006.
  13. A. Pepe , J. Bollen . Between Conjecture and Memento:Shaping a Collective Emotional Perception of the Future . Proceedings of the AAAI 2008 Spring Symposium on Emotion, Personality and Social Behavior , 2008 . A. Pepe, J. Bollen. Between Conjecture and Memento:Shaping a Collective Emotional Perception of the Future. Proceedings of the AAAI 2008 Spring Symposium on Emotion, Personality and Social Behavior, 2008.
  14. A. Podelko . Multiple Dimensions of Performance Requirements. 33rd International Computer Measurement Group Conference , 2007 . A. Podelko. Multiple Dimensions of Performance Requirements. 33rd International Computer Measurement Group Conference, 2007.
  15. V. N. Vapnik . The Nature of Statistical Learning Theory . Springer , 1995 . V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995. · DOI · Paper page
  16. 10.1080/1023697X.1998.10667783 · DOI
  17. Zona Research. The Need for Speed II. Zona Market Bulletin, Issue 05 , 2001 . Zona Research. The Need for Speed II. Zona Market Bulletin, Issue 05, 2001.

Cited by

Sorted by citations

See all 22

Metadata

Authors
Kawai, H., Jatowt, A., Tanaka, K., Kunieda, K., & Yamada, K.
Affiliations
  • NEC C&C Innovation Research Laboratories, Takayamacho, Ikoma, Nara, Japan
  • Kyoto University, Yoshida Honmachi, Sakyo, Kyoto, Japan
Venue
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Publisher
ACM
Published
2010-01-14
Pages
1-10
DOI
10.1145/2108616.2108647
Cited by
22
References
1
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review