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Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals ...
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Extracting Dynamic Evidence Networks
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In: DTIC AND NTIS (2004)
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Challenges in Information Retrieval and Language Modeling
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In: R. Manmatha (2003)
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Challenges in Information Retrieval and Language Modeling
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In: Andrew McCallum (2003)
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TREC-9 Cross-Lingual Retrieval at BBN
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In: DTIC (2000)
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Abstract:
BBN participated only in the cross-language track at TREC-9. We extended the monolingual approach of Miller et al. (1999), which uses hidden Markov models (HMM), by incorporating translation probabilities from Chinese terms to English terms. In our approach, the IR system ranks documents by the probability that a Chinese document D is relevant given an English query Q, P(D is Rel |Q). Using Bayes Rule, and the fact that P(Q) is constant for a given query, and our initial assumption of a uniform a priori probability that a document is relevant, ranking documents according to P(Q|D is Rel) is the same as ranking them according to P(D is Rel|Q). The approach therefore estimates the probability that a query Q is generated, given the document D is relevant. ; Presented at Text REtrieval Conference (9th), TREC-9, held in Gaithersburg, MD, on 13-16 Nov 2000 and published in the proceedings, NIST Special Publication 500-249. Published on line by the National Institute of Standards and Technology.
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Keyword:
*COMPUTATIONAL LINGUISTICS; BAYES THEOREM; CHINESE LANGUAGE; CROSS-LINGUAL INFORMATION RETRIEVAL; ENGLISH LANGUAGE; INDEX TERMS; INFORMATION RETRIEVAL; Information Science; Linguistics; MACHINE TRANSLATION; MARKOV PROCESSES; NATURAL LANGUAGE; Operations Research; SYMPOSIA; TEXT PROCESSING
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA456316 http://www.dtic.mil/docs/citations/ADA456316
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Algorithms That Learn to Extract Information BBN: Description of the Sift System as Used for MUC-7
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In: DTIC (1998)
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BBN: Description of the PLUM System as Used for MUC-6
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In: DTIC (1995)
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