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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 – 5 of 5 1 Algorithms That Learn to Extract Information BBN: Description of the Sift System as Used for MUC-7 Miller, Scott; Crystal, Michael; Fox, Heidi; Ramshaw, Lance; Schwartz, Richard; Stone, Rebecca; Weischedel, Ralph In: DTIC (1998) Abstract: For MUC-7, BBN has for the first time fielded a fully-trained system for NE, TE, and TR; results are all the output of statistical language models trained on annotated data, rather than programs executing handwritten rules. Such trained systems have some significant advantages: 1. They can be easily ported to new domains by simply annotating data with semantic answers. 2. The complex interactions that make rule-based systems difficult to develop and maintain can here be learned automatically from the training data. We believe that the results in this evaluation are evidence that such trained systems, even at their current level of development, can perform roughly on a par with rules hand-tailored by experts. Since MUC-3, BBN has been steadily increasing the proportion of the information extraction process that is statistically trained. Already in MET-1, our name-finding results were the output of a fully statistical, HMM-based model, and that statistical Identifinder(trademark) model was also used for the NE task in MUC-7. For the MUC-7 TE and TR tasks, BBN developed SIFT, a new model that represents a significant further step along this path, replacing PLUM, a system requiring handwritten patterns, with SIFT, a single integrated trained model. ; Presented at the Message Understanding Conference (7th), MUC-7, held in Fairfax, VA on 29 Apr-1 May 1998 and published in proceedings of the same. The original document contains color images. Keyword: *COMPUTATIONAL LINGUISTICS; *CONDITIONING(LEARNING); ALGORITHMS; AUGMENTED PARSE TREES; Computer Programming and Software; DYNAMIC PROGRAMMING; INFORMATION RETRIEVAL; Linguistics; MAXIMUM LIKELIHOOD ESTIMATION; NATURAL LANGUAGE; Operations Research; SIFT(STATISTICS FOR INFORMATION FROM TEXT); STATISTICAL LANGUAGE MODELS; STATISTICAL PROCESSES; Statistics and Probability; SYNTAX URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA460615 http://www.dtic.mil/docs/citations/ADA460615 BASE Hide details 2 Coping with ambiguity and unknown words through probabilistic models Weischedel, Ralph; Meteer, Marie; Schwartz, Richard... In: Using large corpora. - Cambridge, Mass. [u.a.] : MIT Press (1994), 319-342 BLLDB Show details 3 Coping with Ambiguity and Unknown Words through Probabilistic Models Weischedel, Ralph; Meteer, Marie; Schwartz, Richard... In: Computational linguistics. - Cambridge, Mass. : MIT Press 19 (1993) 2, 359-382 OLC Linguistik Show details 4 Using large corpora: II Biber, Douglas (Mitarb.); Brent, Michael R. (Mitarb.); Brown, Peter F. (Mitarb.)... In: Computational linguistics. - Cambridge, Mass. : MIT Press 19 (1993) 2, 219-382 BLLDB Show details 5 Coping with Ambiguity and Unknown Words through Probabilistic Models Weischedel, Ralph; Schwartz, Richard; Palmucci, Jeff... In: DTIC (1993) BASE Show details

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