1. Articles in category: Entailment

    49-58 of 58 « 1 2 3
    1. Semantic relation mining of solid compounds in medical corpora.

      Related Articles Semantic relation mining of solid compounds in medical corpora. Stud Health Technol Inform. 2008;136:217-22 Authors: Kokkinakis D In the context of scientific and technical texts, meaning is usually embedded in noun compounds and the semantic interpretation of these compounds deals with the detection and semantic classification of the relation that holds between the compound's constituents. Semantic relation mining, the technology applied for marking up, interpreting, extracting and classifying relations that hold between pairs of words, is an important enterprise that contribute to deeper means of enhancing document understanding technologies, such as Information Extraction, Question Answering ...
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    2. Improving Question Answering Tasks by Textual Entailment Recognition

      This paper explores a suitable way to integrate a Textual Entailment (TE) system, which detects unidirectional semantic inferences, into Question Answering (QA) tasks. We propose using TE as an answer validation engine to improve QA systems, and we evaluate its performance using the Answer Validation Exercise framework. Results point out that our TE system can improve the QA task considerably. Content Type Book ChapterDOI 10.1007/978-3-540-69858-6_37Authors Óscar Ferrández, University of Alicante Natural Language Processing and Information Systems Group Department of Computing Languages and SystemsRafael Muñoz, University of Alicante Natural Language Processing and Information Systems Group Department of Computing Languages ...
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    3. Systems, methods, and software for hyperlinking names

      Hyperlinking or associating documents to other documents based on the names of people in the documents has become more desirable. Although there is an automated system for installing such hyperlinks into judicial opinions, the system is not generally applicable to other types of names and documents, nor well suited to determine hyperlinks for names that might refer to two or more similarly named persons. Accordingly, the inventor devised systems, methods, and software that facilitate hyperlinking names in documents, regardless of type. One exemplary system includes a descriptor module and a linking module. The descriptor module develops descriptive patterns for selecting ...
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    4. Detecting Expected Answer Relations through Textual Entailment

      This paper presents a novel approach to Question Answering over structured data, which is based on Textual Entailment recognition. The main idea is that the QA problem can be recast as an entailment problem, where the text (T) is the question and the hypothesis (H) is a relational pattern, which is associated to “instructions” for retrieving the answer to the question. In this framework, given a question Q and a set of answer patterns P, the basic operation is to select those patterns in P that are entailed by Q. We report on a number of experiments which show the ...
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    5. 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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    6. 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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    7. 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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    8. 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
    49-58 of 58 « 1 2 3
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