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Automatic Labeling of Semantic Roles with a Dependency Parser in Hungarian Economic Texts

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Text, Speech, and Dialogue (TSD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9302))

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Abstract

The events in natural language texts and the detection and analysis of the semantic relationships or semantic roles of these events play an important role in several natural language processing (NLP) applications such as summarization and question answering. In this study we introduce a machine learning-based approach that can automatically label semantic roles in Hungarian texts by applying a dependency parser. In our study we dealt with the areas of purchases of companies and news from stock markets. For the tasks we applied binary classifiers based on rich feature sets. In this study we introduce new methods for this application area. According to our best knowledge, this is the first result for automatic labeling of semantic roles with a dependency parser in Hungarian texts, for domain specific roles. Having evaluated them on test databases, our algorithms achieve competitive results as compared to the current English results.

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Author information

Authors and Affiliations

  1. College of Szolnok, Technical, Agricultural and Economic Analysis Department, Szolnok, Hungary

    Zoltán Subecz

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  1. Zoltán Subecz
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Correspondence to Zoltán Subecz .

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Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Pavel Král

  2. University of West Bohemia, Pilsen, Czech Republic

    Václav Matoušek

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Subecz, Z. (2015). Automatic Labeling of Semantic Roles with a Dependency Parser in Hungarian Economic Texts. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_30

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  • DOI: https://doi.org/10.1007/978-3-319-24033-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24032-9

  • Online ISBN: 978-3-319-24033-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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