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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 Comparative Experiments on Large Vocabulary Speech Recognition Schwartz, Richard; Anastasakos, Tasos; Kubala, Francis... In: DTIC (1993) BASE Show details 2 BBN PLUM: MUC-4 Test Results and Analysis Weischedel, Ralph; Ayuso, Damaris; Boisen, Sean... In: DTIC (1992) BASE Show details 3 Development of a Spoken Language System Austin, S.; Ayuso, D.; Barry, C.... In: DTIC AND NTIS (1992) BASE Show details 4 BBN HARC and DELPHI Results on the ATIS Benchmarks - February 1991 Austin, S.; Ayuso, D.; Bates, M.... In: DTIC (1991) BASE Show details 5 BBN PLUM: MUC-3 Test Results and Analysis Weischedel, Ralph; Ayuso, Damaris; Boisen, Sean... In: DTIC (1991) BASE Show details 6 Adaptive Natural Language Processing Ayuso, Damaris; Boisen, Sean; Bobrow, Robert; Gish, Herbert; Ingria, Robert In: DTIC AND NTIS (1991) Abstract: A handful of special purpose systems have been successfully deployed to extract prespecified kinds of data from text. The limitation to widespread deployment of such systems is their assumption of a large volume of handcrafted, domain-dependent, and language-dependent knowledge in the form of rules. A new approach is to add automatically trainable probabilistic language models to linguistically based analysis. This offers several potential advantages: (1) Trainability by finding patterns in a large corpus, rather than handcrafting such patterns. (2) Improvability be re-estimating probabilities based on a user marking correct and incorrect output on a test set. (3) More accurate selection among interpretations when more than one is produced. (4) Robustness by finding the most likely partial interpretation when no complete interpretation can be found. Keyword: *INFORMATION PROCESSING; *MESSAGE PROCESSING; *NATURAL LANGUAGE; *VOLUME; ACCURACY; ADAPTIVE SYSTEMS; CLASSIFICATION; INFORMATION PROCESSING; Information Science; LANGUAGE; Linguistics; MARKERS; MODELS; NATURAL LANGUAGE; PARSERS; PATTERNS; PE61101E; PILOT STUDIES; PROBABILITY; SELECTION; SEMANTICS; TEST SETS; TEXT; TRAINING; USER NEEDS; WURLG302QA01 URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA241336 http://www.dtic.mil/docs/citations/ADA241336 BASE Hide details 7 Integration of Speech and Natural Language Boisen, S.; Chow, Y.; Haas, A.... In: DTIC AND NTIS (1989) BASE Show details 8 Combining Multiple Knowledge Sources for Continuous Speech Recognition Kimball, O.; Derr, A.; Chow, Y-L.... In: DTIC AND NTIS (1989) BASE Show details

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