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In late 2008, archival staff went on a one-month strike after a scholar from an Ivy League institution made a routine request for document retrieval.
Weirdness [42] was initially proposed for document retrieval system.
For document retrieval, the click-count improved by 2.71 clicks.
We now propose using the same algorithm for document retrieval.
For document retrieval, 100 test subjects tested the system with 10 queries per subject.
The click-count for simple image retrieval queries improved by 1.44 clicks and by 2.63 clicks for document retrieval.
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Ground truth test collections for the evaluation of document retrieval systems have sets of queries and documents for which human experts have judged the relevance of document query pairs.
Collectively, these factors can be used to provide a useful framework for, or taxonomy of, document retrieval, and highlight some of the fundamental issues facing the design and development of commercial document retrieval systems.
Table 1 Search-on-speech applications Type of result (output) Type of query (input) Text Query Document SDR QbE-SDR Document+Position STD/KWS QbE-STD 'SDR' stands for Spoken Document Retrieval, 'STD' for Spoken Term Detection, 'KWS' for Keyword Spotting, and 'QbE' for Query-by-Example.
In this paper, we present a new GUI for structured document retrieval specifically designed for hierarchically structured documents.
Several international evaluation campaigns, like the TREC NIST evaluations for Spoken Document Retrieval (SDR) [1], the ESTER evaluations campaign for Rich Transcription (RT) in French [2], and the COST278 evaluation for segmentation and speaker clustering in multi-lingual domain [3], have already been proposed to face this task in the past.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com