In this approach to named entity recognition, a recurrent neural network, known as Long Short-Term Memory, is applied. The network is trained to perform 2 passes on each sentence, outputting its decisions on the second pass. The first pass is used to acquire information for disambiguation during the second pass. SARD-NET, a self-organising map for sequences is used to generate representations for the lexical items presented to the LSTM network, whilst orthogonal representations are used to represent the part of speech and chunk tags.
REF was the first to exploit LSTM for sequence labeling.
Named Entity Recognition With Long Short-Term Memory
Conference On Computational Natural Language Learning CoNLL
Computer Science