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Building an information retrieval test collection for spontaneous conversational speech
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Sheffield, United Kingdom
SESSION: Test collections table of contents
Pages: 41 - 48  
Year of Publication: 2004
ISBN:1-58113-881-4
Authors
Douglas W. Oard  University of Maryland, College Park, MD
Dagobert Soergel  University of Maryland, College Park, MD
David Doermann  University of Maryland, College Park, MD
Xiaoli Huang  University of Maryland, College Park, MD
G. Craig Murray  University of Maryland, College Park, MD
Jianqiang Wang  University of Maryland, College Park, MD
Bhuvana Ramabhadran  IBM T.J. Watson Research Center, Yorktown Heights, NY
Martin Franz  IBM T.J. Watson Research Center, Yorktown Heights, NY
Samuel Gustman  Visual History Foundation, Los Angeles, CA
James Mayfield  The Johns Hopkins University
Liliya Kharevych  California Institute of Technology
Stephanie Strassel  Linguistic Data Consortium
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

Test collections model use cases in ways that facilitate evaluation of information retrieval systems. This paper describes the use of search-guided relevance assessment to create a test collection for retrieval of spontaneous conversational speech. Approximately 10,000 thematically coherent segments were manually identified in 625 hours of oral history interviews with 246 individuals. Automatic speech recognition results, manually prepared summaries, controlled vocabulary indexing, and name authority control are available for every segment. Those features were leveraged by a team of four relevance assessors to identify topically relevant segments for 28 topics developed from actual user requests. Search-guided assessment yielded sufficient inter-annotator agreement to support formative evaluation during system development. Baseline results for ranked retrieval are presented to illustrate use of the collection.


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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Collaborative Colleagues:
Douglas W. Oard: colleagues
Dagobert Soergel: colleagues
David Doermann: colleagues
Xiaoli Huang: colleagues
G. Craig Murray: colleagues
Jianqiang Wang: colleagues
Bhuvana Ramabhadran: colleagues
Martin Franz: colleagues
Samuel Gustman: colleagues
James Mayfield: colleagues
Liliya Kharevych: colleagues
Stephanie Strassel: colleagues