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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Utility of routine follow-up head CT scanning after mild traumatic brain injury: a systematic review of the literature Stippler, Martina; Smith, Carl; McLean, A Robb. - : British Medical Journal Publishing Group, 2012 BASE Show details 2 Utility of routine follow-up head CT scanning after mild traumatic brain injury: a systematic review of the literature Stippler, Martina; Smith, Carl; McLean, A Robb. - : BMJ Publishing Group Ltd, 2012 BASE Show details 3 Coupling Semi-Supervised Learning of Categories and Relations ... Carlson, Andrew; Betteridge, Justin; Estevam Hruschka. - : Carnegie Mellon University, 2009 BASE Show details 4 Coupling Semi-Supervised Learning of Categories and Relations ... Carlson, Andrew; Betteridge, Justin; Estevam Hruschka. - : Carnegie Mellon University, 2009 BASE Show details 5 Predicting human brain activity associated with the meanings of nouns Mitchell, Tom M.; Shinkareva, Svetlana V.; Carlson, Andrew... In: Science. - Washington, DC : AAAS, American Assoc. for the Advancement of Science 320 (2008) 5880, 1191-1195 BLLDB Show details 6 Predicting human brain activity associated with the meanings of nouns ... Mitchell, Tom M.; Shinkareva, Svetlana V.; Carlson, Andrew. - : Carnegie Mellon University, 2008 BASE Show details 7 Predicting human brain activity associated with the meanings of nouns ... Mitchell, Tom M.; Shinkareva, Svetlana V.; Carlson, Andrew. - : Carnegie Mellon University, 2008 BASE Show details 8 Data Analysis Project: Leveraging Massive Textual Corpora Using n-Gram Statistics Carlson, Andrew; Mitchell, Tom M.; Fette, Ian In: DTIC (2008) Abstract: We study methods of efficiently leveraging massive textual corpora through n-gram statistics. Specifically, we explore algorithms that use a database of frequency counts for sequences of tokens in a teraword Web corpus to correct spelling mistakes and to extract a list of instances of some category given only the name of the target category. For spelling correction, we use a novel correction algorithm and demonstrate high accuracy in correcting both real-word errors and non-word errors. For category extraction, we show promising preliminary results for a variety of categories. We conclude that n-gram statistics provide an efficient way to use information contained in a massive corpus of text using relatively simple algorithms. The report ends with a reflection on problems encountered, possible solutions, and future work. ; Sponsored in part by DARPA. Keyword: *ALGORITHMS; *CORRECTIONS; *N-GRAM STATISTICS; *NATURAL LANGUAGE; *TEXT PROCESSING; *TEXTUAL CORPORA; Cybernetics; DATA PROCESSING; ERRORS; EXTRACTION; INTERNET; LEARNING MACHINES; Linguistics; SPELLING MISTAKES; STATISTICS URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA486165 http://www.dtic.mil/docs/citations/ADA486165 BASE Hide details

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