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.
|Title of host publication||COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings|
|Editors||Emily M. Bender, Leon Derczynski, Pierre Isabelle|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||12|
|Publication status||Published - 2018|
|Event||27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States|
Duration: 2018 Aug 20 → 2018 Aug 26
|Name||COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings|
|Conference||27th International Conference on Computational Linguistics, COLING 2018|
|Period||18/8/20 → 18/8/26|
Bibliographical noteFunding 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).
© 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.
ASJC Scopus subject areas
- Language and Linguistics
- Computational Theory and Mathematics
- Linguistics and Language