Word sense disambiguation with very large neural networks extracted from machine readable dictionaries

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Abstract
In this paper, we describe a means for automatically building very large neural networks (VLNNs) from definition texts in machine-readable dictionaries, and demonstrate the use of these networks for word sense disambiguation. Our method brings together two earlier, independent approaches to word sense disambiguation: the use of machine-readable dictionaries and spreading and activation models. The automatic construction of VLNNs enables real-size experiments with neural networks for natural language processing, which in turn provides insight into their behaviour and design and can lead to possible improvements.

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