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Marasović, A. and Frank, A. (2016). Multilingual modal sense classification using a convolutional neural network. In Proceedings of the 1st Workshop on Representation Learning for NLP, pages 111–120, August 11, 2016, Berlin, Germany. Association for Computational Linguistics.
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\r\nModal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal’s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We bench-mark the CNN on a standard WSD task,where it compares favorably to models using sense-disambiguated target vectors.
\r\n(Marasović and Frank, 2016)
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Link to the dataset: https://doi.org/10.11588/data/JEESIQ (heiDATA)
TensorFlow
\r\nVersion: r0.12
\r\nLink to the https://github.com/tensorflow/tensorflow/tree/r0.12
\r\nLicense: Apache License 2.0
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\r\nThe Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling” (LiMo) is a cooperative research project between the Leibniz Institute for the German Language (Leibniz-Institut für Deutsche Sprache, IDS) in Mannheim and the Department of Computational Linguistics at Heidelberg University (ICL). The general aims of the project are to develop new methods, models, and tools for compiling and analysing automatically large German textual corpora covering different domains, genres and language varieties.
\r\nThe project is supported by funds from the Baden-Württemberg Ministry of Science, Research and the Arts and the Leibniz Association together with funds provided by the Leibniz Institute for the German Language and Heidelberg University.
\r\nFunding Period: 2015 – 2020
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