TY - GEN
T1 - Character-level feature extraction with densely connected networks
AU - Lee, Chanhee
AU - Kim, Young Bum
AU - Lee, Dongyub
AU - Lim, Heui Seok
N1 - Funding Information:
This research was supported by the MSIT (Ministry of Science and ICT), South Korea, under the ITRC (Information Technology Research Center) support program (”Research and Development of Human-Inspired Multiple Intelligence”) supervised by the IITP (Institute for Information & Communications Technology Promotion). Additionally, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the South Korean government (MSIP) (No. NRF-2016R1A2B2015912).
Publisher Copyright:
© 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Generating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generating hand-crafted features, methods that utilize neural architectures such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) to automatically extract such features have been proposed and have shown great results. However, CNN generates position-independent features, and RNN is slow since it needs to process the characters sequentially. In this paper, we propose a novel method of using a densely connected network to automatically extract character-level features. The proposed method does not require any language or task specific assumptions, and shows robustness and effectiveness while being faster than CNN- or RNN-based methods. Evaluating this method on three sequence labeling tasks - slot tagging, Part-of-Speech (POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art performance with a 96.62 F1-score and 97.73% accuracy on slot tagging and POS tagging, respectively, and comparable performance to the state-of-the-art 91.13 F1-score on NER.
AB - Generating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generating hand-crafted features, methods that utilize neural architectures such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) to automatically extract such features have been proposed and have shown great results. However, CNN generates position-independent features, and RNN is slow since it needs to process the characters sequentially. In this paper, we propose a novel method of using a densely connected network to automatically extract character-level features. The proposed method does not require any language or task specific assumptions, and shows robustness and effectiveness while being faster than CNN- or RNN-based methods. Evaluating this method on three sequence labeling tasks - slot tagging, Part-of-Speech (POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art performance with a 96.62 F1-score and 97.73% accuracy on slot tagging and POS tagging, respectively, and comparable performance to the state-of-the-art 91.13 F1-score on NER.
UR - http://www.scopus.com/inward/record.url?scp=85077447546&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85077447546
T3 - COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings
SP - 3228
EP - 3239
BT - COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings
A2 - Bender, Emily M.
A2 - Derczynski, Leon
A2 - Isabelle, Pierre
PB - Association for Computational Linguistics (ACL)
T2 - 27th International Conference on Computational Linguistics, COLING 2018
Y2 - 20 August 2018 through 26 August 2018
ER -