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.
@article{arxiv.1608.05243,
title = {Multilingual Modal Sense Classification using a Convolutional Neural Network},
author = {Ana Marasović and Anette Frank},
journal= {arXiv preprint arXiv:1608.05243},
year = {2016}
}
Comments
Final version, accepted at the 1st Workshop on Representation Learning for NLP, held in conjunction with ACL 2016