Disambiguating Spatial Prepositions Using Deep Convolutional Networks

We address the coarse-grained disambiguation of the spatial prepositions as the first step towards spatial role labeling using deep learning models. We propose a hybrid feature of word embeddings and linguistic features, and compare its performance against a set of linguistic features, pre-trained word embeddings, and corpus-trained embeddings using seven classical machine learning classifiers and two deep learning models. We also compile a dataset of 43, 129 sample sentences from Pattern Dictionary of English Prepositions (PDEP). The comprehensive experimental results suggest that the combination of the hybrid feature and a convolutional neural network outperforms state-of-the-art methods and reaches the accuracy of 94. 21% and F1-score of 0. 9398.

Authors

• Kaveh Hassani
• Won-Sook Lee

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