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Echo State Networks for Named Entity Recognition

: Ramamurthy, Rajkumar; Stenzel, Robin; Sifa, Rafet; Ladi, Anna; Bauckhage, Christian


Tetko, I.V.:
Artificial Neural Networks and Machine Learning - ICANN 2019. Workshop and Special Sessions. Proceedings : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11731)
ISBN: 978-3-030-30492-8 (Print)
ISBN: 978-3-030-30493-5 (Online)
International Conference on Artificial Neural Networks (ICANN) <28, 2019, Munich>
Conference Paper
Fraunhofer IAIS ()

This paper explores a simple method for obtaining contextual word representations. Recently, it was shown that random sentence representations obtained from echo state networks (ESNs) were able to achieve near state-of-the-art results in several sequence classification tasks. We explore a similar direction while considering a sequence labeling task specifically named entity recognition (NER). The idea is to simply use reservoir states of an ESN as contextual word embeddings by passing pre-trained word-embeddings as its input. Experimental results show that our approach achieves competitive results in terms of accuracy and faster training times when compared to state-of-the-art methods. In addition, we provide an empirical evaluation of hyper-parameters that influence this performance.