On the other hand, while encoding definitional information using deep learning techniques has been addressed in the past (Hill et al., 2015;Noraset et al., 2016), to the best of our knowledge no previous work has tackled the problem of DE by reconciling both the linguistic lessons learned in the past decades (e.g., the importance of lexico syntactic patterns or long-distance relations between definiendum and definiens) 1 and the processing potential of neural networks.
we propose to bridge this gap by learning high level features over candidate definitions via convolutional filters, and then apply recurrent neural networks to learn long term dependencies over these feature maps.