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      Coupled Hierarchical IR and Stochastic Models for Surface Information Extraction.

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      20th Annual BCS-IRSG Colloquium on IR (IRSG)

      BCS-IRSG Colloquium on IR

      25-27 March 1998

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          Abstract

          We present in this paper a combination of Machine Learning based Information Retrieval (IR) techniques and stochastic language modelling in a hierarchical system that extracts surface information from text. At the lowest level of this hierarchy, documents and paragraphs are successively routed with IR techniques. At the top level, a stochastic language model extracts the most relevant phrases, and labels the type of information they contain. The approach and preliminary results are demonstrated on a subset of the MUC-6 Scenario Templates task.

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          Most cited references 3

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          A scaled conjugate gradient algorithm for fast supervised learning

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            Document and Passage Retrieval Based on Hidden Markov Models

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              The generic information extraction system

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                Author and article information

                Contributors
                Conference
                March 1998
                March 1998
                : 1-9
                Affiliations
                LIP6, Université Pierre et Marie Curie,

                4, place Jussieu F-75252,

                PARIS cedex 05 (F).
                Article
                10.14236/ewic/IRSG1998.16
                © Hugo Zaragoza et al. Published by BCS Learning and Development Ltd. 20th Annual BCS-IRSG Colloquium on IR, Autrans, France

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                20th Annual BCS-IRSG Colloquium on IR
                IRSG
                20
                Autrans, France
                25-27 March 1998
                Electronic Workshops in Computing (eWiC)
                BCS-IRSG Colloquium on IR
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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