Named entity recognition with long short-term memory

Authors:
James Hammerton
Affiliations:
University of Groningen, Groningen, The Netherlands
Venue:
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Year:
2003

Citing 3
Cited 1

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Introduction to the CoNLL-2003 shared task: language-independent named entity recognition

CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4

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Abstract

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. SARDNET, 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.