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        <title>Character-Level Feature Extraction with Densely Connected Networks</title>
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        <namePart type="given">Chanhee</namePart>
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    <abstract>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.</abstract>
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        <url>https://aclanthology.org/C18-1273</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Character-Level Feature Extraction with Densely Connected Networks
%A Lee, Chanhee
%A Kim, Young-Bum
%A Lee, Dongyub
%A Lim, Heuiseok
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F lee-etal-2018-character
%X 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.
%U https://aclanthology.org/C18-1273
%P 3228-3239

Markdown (Informal)

[Character-Level Feature Extraction with Densely Connected Networks](https://aclanthology.org/C18-1273) (Lee et al., COLING 2018)

• Character-Level Feature Extraction with Densely Connected Networks (Lee et al., COLING 2018)

ACL

• Chanhee Lee, Young-Bum Kim, Dongyub Lee, and Heuiseok Lim. 2018. Character-Level Feature Extraction with Densely Connected Networks. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3228–3239, Santa Fe, New Mexico, USA. Association for Computational Linguistics.