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NLM109954068 |
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DE-627 |
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20231222152438.0 |
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tu |
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231222s2000 xx ||||| 00| ||eng c |
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|a pubmed24n0367.xml
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|a (DE-627)NLM109954068
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|a (NLM)11079847
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Berrios, D C
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Automated indexing for full text information retrieval
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|c 2000
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 08.03.2001
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a We report our experience with a statistically based method of generating sentence-level indexing based on identified UMLS concepts and query and vector-space models. We evaluated the system using the consensus markup of two domain experts as the gold standard. UMLS concepts identified both from HTML headings and in paragraph text were valuable in proposing markup. Using both sources of concepts, the model proposed the correct set of concepts in the form of a query prototype 71% of the time. The correct query prototype was ranked first or second in 79% of cases
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| 650 |
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|a Evaluation Study
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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 773 |
0 |
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|i Enthalten in
|t Proceedings. AMIA Symposium
|d 1998
|g (2000) vom: 01., Seite 71-5
|w (DE-627)NLM098642928
|x 1531-605X
|7 nnns
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| 773 |
1 |
8 |
|g year:2000
|g day:01
|g pages:71-5
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| 912 |
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|a GBV_USEFLAG_A
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| 912 |
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|a SYSFLAG_A
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| 912 |
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|a GBV_NLM
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| 912 |
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|a GBV_ILN_350
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| 951 |
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|a AR
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| 952 |
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|j 2000
|b 01
|h 71-5
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