In this paper, we describe a means for automatically building very large neural networks (VLNNs) from definition texts in machine-readable dictionaries, and demonslrate 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 behavior and design and can lead to possible improvements.
Veronis et al. REF automatically build a very large Neural Network from definition text in machine readable dictionaries and used this network for word sense disambiguation.
Word Sense Disambiguation With Very Large Neural Networks Extracted From Machine Readable Dictionaries
International Conference On Computational Linguistics
Computer Science