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        <namePart type="given">Svetla</namePart>
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        <namePart type="given">Ivelina</namePart>
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    <name type="personal">
        <namePart type="given">Galia</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
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    <abstract>Shallow text analysis (Text Mining) uses mainly Information Extraction techniques. The low resource languages do not allow application of such traditional techniques with sufficient accuracy and recall on big data. In contrast, Data Mining approaches provide an opportunity to make deep analysis and to discover new knowledge. Frequent pattern mining approaches are used mainly for structured information in databases and are a quite challenging task in text mining. Unfortunately, most frequent pattern mining approaches do not use contextual information for extracted patterns: general patterns are extracted regardless of the context. We propose a method that processes raw informal texts (from health discussion forums) and formal texts (outpatient records) in Bulgarian language. In addition we use some context information and small terminological lexicons to generalize extracted frequent patterns. This allows to map informal expression of medical terminology to the formal one and to generate automatically resources.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_019</identifier>
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%0 Conference Proceedings
%T Mining Association Rules from Clinical Narratives
%A Boytcheva, Svetla
%A Nikolova, Ivelina
%A Angelova, Galia
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F boytcheva-etal-2017-mining
%X Shallow text analysis (Text Mining) uses mainly Information Extraction techniques. The low resource languages do not allow application of such traditional techniques with sufficient accuracy and recall on big data. In contrast, Data Mining approaches provide an opportunity to make deep analysis and to discover new knowledge. Frequent pattern mining approaches are used mainly for structured information in databases and are a quite challenging task in text mining. Unfortunately, most frequent pattern mining approaches do not use contextual information for extracted patterns: general patterns are extracted regardless of the context. We propose a method that processes raw informal texts (from health discussion forums) and formal texts (outpatient records) in Bulgarian language. In addition we use some context information and small terminological lexicons to generalize extracted frequent patterns. This allows to map informal expression of medical terminology to the formal one and to generate automatically resources.
%R 10.26615/978-954-452-049-6_019
%U https://doi.org/10.26615/978-954-452-049-6_019
%P 130-138

Markdown (Informal)

[Mining Association Rules from Clinical Narratives](https://doi.org/10.26615/978-954-452-049-6_019) (Boytcheva et al., RANLP 2017)

• Mining Association Rules from Clinical Narratives (Boytcheva et al., RANLP 2017)

ACL

• Svetla Boytcheva, Ivelina Nikolova, and Galia Angelova. 2017. Mining Association Rules from Clinical Narratives. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 130–138, Varna, Bulgaria. INCOMA Ltd..