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Sequence Models for Automatic Highlighting and Surface Information Extraction

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21st Annual BCS-IRSG Colloquium on IR (IRSG)

IR Research

19-20 April 1999

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      With the increase of textual information available electronically, we assist to a great diversification of the demands on Information Retrieval (IR) and Information Extraction (IE) systems. In this paper we apply Machine Learning techniques of sequence analysis to the tasks of highlighting and labeling text with respect to an information extraction task. Specifically, dynamic probability models are used. Like IR systems, they use little semantics, are fully trainable and do not require any knowledge representation of the domain. Unlike IR approaches, documents are considered as a dynamic sequence of words. Furthermore, additional word information is naturally included in the representation. Models are evaluated on a sub-task of the MUC6 Scenario Template corpus. When morpho-syntactic word information is introduced into the representation, an increase in performances is observed.

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      Document and Passage Retrieval Based on Hidden Markov Models


        Author and article information

        LIP6, University of Paris 6,

        case 169, 4 Place Jussieu, F-75252

        Paris cedex 05, France.
        April 1999
        April 1999
        : 1-9
        © Massih-Reza Amini et al. Published by BCS Learning and Development Ltd. 21st Annual BCS-IRSG Colloquium on IR, Glasgow

        This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit

        21st Annual BCS-IRSG Colloquium on IR
        19-20 April 1999
        Electronic Workshops in Computing (eWiC)
        IR Research
        Product Information: 1477-9358 BCS Learning & Development
        Self URI (journal page):
        Electronic Workshops in Computing


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