Multilingual Modal Sense Classification using a Convolutional Neural Network

Ana Marasović; Anette Frank

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arXiv · 1608.05243

Multilingual Modal Sense Classification using a Convolutional Neural Network

Ana Marasović·Anette Frank

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Abstract

Modal sense classification (MSC) is a special WSD task that depends on the meaning 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 benchmark the CNN on a standard WSD task, where it compares favorably to models using sense-disambiguated target vectors.

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Ana Marasović, Anette Frank. 2016-08-18. Multilingual Modal Sense Classification using a Convolutional Neural Network. https://arxiv.org/abs/1608.05243

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