Shallow parsing with long short-term memory

Applying Artificial Neural Networks (ANNs) to language learning has been an active area of research in connectionism. However much of this work has involved small and/or artificially created data sets, whilst other approaches to language learning are now routinely applied to large real-world datasets containing hundreds of thousands of words or more, thus raising the question of how ANNs might scale-up. This paper describes recent work on shallow parsing of real world texts using a recurrent neural network(RNN) architecture called Long Short-Term Memory (LSTM)(1).