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        <title>Graph-Based Approach to Recognizing CST Relations in Polish Texts</title>
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        <namePart type="given">Paweł</namePart>
        <namePart type="family">Kędzia</namePart>
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    <name type="personal">
        <namePart type="given">Arkadiusz</namePart>
        <namePart type="family">Janz</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>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper presents an supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. In the proposed, graph-based representation is constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relation extracted from text. Similarity between sentences is calculated from graph, and the similarity values are input to classifiers trained by Logistic Model Tree. Several different configurations of graph, as well as graph similarity methods were analysed for this tasks. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_048</identifier>
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%0 Conference Proceedings
%T Graph-Based Approach to Recognizing CST Relations in Polish Texts
%A Kędzia, Paweł
%A Piasecki, Maciej
%A Janz, Arkadiusz
%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 kedzia-etal-2017-graph
%X This paper presents an supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. In the proposed, graph-based representation is constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relation extracted from text. Similarity between sentences is calculated from graph, and the similarity values are input to classifiers trained by Logistic Model Tree. Several different configurations of graph, as well as graph similarity methods were analysed for this tasks. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.
%R 10.26615/978-954-452-049-6_048
%U https://doi.org/10.26615/978-954-452-049-6_048
%P 363-371

Markdown (Informal)

[Graph-Based Approach to Recognizing CST Relations in Polish Texts](https://doi.org/10.26615/978-954-452-049-6_048) (Kędzia et al., RANLP 2017)

• Graph-Based Approach to Recognizing CST Relations in Polish Texts (Kędzia et al., RANLP 2017)

ACL

• Paweł Kędzia, Maciej Piasecki, and Arkadiusz Janz. 2017. Graph-Based Approach to Recognizing CST Relations in Polish Texts. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 363–371, Varna, Bulgaria. INCOMA Ltd..