1. Articles in category: Entailment

    49-52 of 52 « 1 2 3
    1. Memory-Efficient Inference in Relational Domains

      Memory-Efficient Inference in Relational Domains Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {parag, pedrod}@cs.washington.edu Parag Singla Pedro Domingos Abstract Propositionalization of a first-order theory followed by satisfiability testing has proved to be a remarkably efficient approach to inference in relational domains such as planning (Kautz & Selman 1996) and verification (Jackson 2000). More recently, weighted satisfiability solve
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    2. Markov Logic

      Pedro Domingos1 , Stanley Kok1 , Daniel Lowd1 , Hoifung Poon1, Matthew Richardson2, and Parag Singla1 Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {pedrod, koks, lowd, hoifung, parag}@cs.washington.edu 2 Microsoft Research Redmond, WA 98052 mattri@microsoft.com 1 Abstract. Most real-world machine learning problems have both statistical and relational aspects. Thus learners need representations that combine probability and re
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    3. Systems and methods for detecting entailment and contradiction

      Techniques are provided for detecting entailment and contradiction. Packed knowledge representations for a premise and conclusion text are determined comprising facts about the relationships between concept and/or context denoting terms. Concept and context alignments are performed based on alignments scores. A union is determined. Terms are marked as to their origin and conclusion text terms replaced with by corresponding terms from the premise text. Subsumption and specificity, instantiability, spatio-temporal and relationship based packed rewrite rules are applied in conjunction with the context denoting facts to remove entailed terms and to mark contradictory facts within the union. Entailment is indicated ...
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    4. Identifying Patient Smoking Status from Medical Discharge Records.

      Related Articles Identifying Patient Smoking Status from Medical Discharge Records. J Am Med Inform Assoc. 2008 January-February;15(1):14-24 Authors: Uzuner O, Goldstein I, Luo Y, Kohane I The authors organized a Natural Language Processing (NLP) challenge on automatically determining the smoking status of patients from information found in their discharge records. This challenge was issued as a part of the i2b2 (Informatics for Integrating Biology to the Bedside) project, to survey, facilitate, and examine studies in medical language understanding for clinical narratives. This article describes the smoking challenge, details the data and the annotation process, explains the evaluation ...
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      Mentions: Uzuner O Albany
    49-52 of 52 « 1 2 3
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