Auflistung nach Autor:in "Arai, Kohei"

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    Publikation
    A serious game for teaching genetic algorithms
    (2021) Moser, Lars; Saner, Kevin; Oggier, Vincent; Hanne, Thomas; Arai, Kohei [in: Proceedings of the Future Technologies Conference (FTC) 2021]
    04B - Beitrag Konferenzschrift
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    Publikation
    Fact checking: detection of check worthy statements through support vector machine and feed forward neural network
    (Springer, 2021) Ahmed, Sajjad; Balla, Klestia; Hinkelmann, Knut; Corradini, Flavio; Arai, Kohei [in: Advances in information and communication. Proceedings of the 2021 Future of Information and Communication Conference (FICC)]
    Detection of check-worthy statements is a subtask in the fact-checking process, automation of which would decrease the time and burden required to fact-check a statement. This paper proposes an approach focused on the classification of statements into check-worthy and not check-worthy. For the current paper, a dataset is constructed by consulting different fact-checking organizations. It contains debates and speeches in the domain of politics. Thus, even the ability of check worthy approach is evaluated on this domain. It starts with extracting sentence-level and context features from the sentences, and classifying them based on these features. The feature set and context were chosen after several experiments, based on how well they differentiate check-worthy statements. The findings indicated that the context in the approach gives considerable contribution in the classification, while also using more general features to capture information from the sentences. The results were analyzed by examining all features used, assessing their contribution in classification, and how well the approach performs in speeches and debates separately to detect the check worthy statements to reduce the time and burden of fact checking process.
    04B - Beitrag Konferenzschrift
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    Publikation
    FLIE: form labeling for information extraction
    (2021) Pustulka, Elzbieta; Hanne, Thomas; Gachnang, Phillip; Biafora, Pasquale; Arai, Kohei; Kapoor, Supriya; Bhatia, Rahul [in: Proceedings of the Future Technologies Conference (FTC) 2020]
    Information extraction (IE) from forms remains an unsolved problem, with some exceptions, like bills. Forms are complex and the templates are often unstable, due to the injection of advertising, extra conditions, or document merging. Our scenario deals with insurance forms used by brokers in Switzerland. Here, each combination of insurer, insurance type and language results in a new document layout, leading to a few hundred document types. To help brokers extract data from policies, we developed a new labeling method, called FLIE (form labeling for information extraction). FLIE first assigns a document to a cluster, grouping by language, insurer, and insurance type. It then labels the layout. To produce training data, the user annotates a sample document by hand, adding attribute names, i.e. provides a mapping. FLIE applies machine learning to propagate the mapping and extracts information. Our results are based on 24 Swiss policies in German: UVG (mandatory accident insurance), KTG (sick pay insurance), and UVGZ (optional accident insurance). Our solution has an accuracy of around 84-89%. It is currently being extended to other policy types and languages.
    04B - Beitrag Konferenzschrift