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Detecting events with date and place information in unstructured text
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Source International Conference on Digital Libraries archive
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries table of contents
Portland, Oregon, USA
SESSION: Searching across language, time, and space table of contents
Pages: 191 - 196  
Year of Publication: 2002
ISBN:1-58113-513-0
Author
David A. Smith  Tufts University, Medford, MA
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 5,   Downloads (12 Months): 43,   Citation Count: 6
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ABSTRACT

Digital libraries of historical documents provide a wealth of information about past events, often in unstructured form. Once dates and place names are identified and disambiguated, using methods that can differ by genre, we examine collocations to detect events. Collocations can be ranked by several measures, which vary in effectiveness according to type of events, but the log-likelihood measure (-2 log &lgr;) offers a reasonable balance between frequently and infrequently mentioned events and between larger and smaller spatial and temporal ranges. Significant date-place collocations can be displayed on timelines and maps as an interface to digital libraries. More detailed displays can highlight key names and phrases associated with a given event.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Dana McKay and Sally Jo Cunningham. Mining dates from historical documents. Technical report, Department of Computer Science, University of Waikato, 2000
 
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