Overview of character-based models for natural language processing

Heike Adel; Ehsaneddin Asgari; Hinrich Schütze

doi:10.1007/978-3-319-77113-7_1

Overview of character-based models for natural language processing

Heike Adel, Ehsaneddin Asgari, Hinrich Schütze^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Character-based models become more and more popular for different natural language processing task, especially due to the success of neural networks. They provide the possibility of directly model text sequences without the need of tokenization and, therefore, enhance the traditional preprocessing pipeline. This paper provides an overview of character-based models for a variety of natural language processing tasks. We group existing work in three categories: tokenization-based approaches, bag-of-n-gram models and end-to-end models. For each category, we present prominent examples of studies with a particular focus on recent character-based deep learning work.

Original language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers
Editors	Alexander Gelbukh
Publisher	Springer Verlag
Pages	3-16
Number of pages	14
ISBN (Print)	9783319771120
DOIs	https://doi.org/10.1007/978-3-319-77113-7_1
Publication status	Published - 2018
Externally published	Yes
Event	18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 - Budapest, Hungary Duration: 17 Apr 2017 → 23 Apr 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10761 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017
Country/Territory	Hungary
City	Budapest
Period	17/04/17 → 23/04/17

Keywords

Document representation
Feature selection
Language models
Natural language generation
Natural language processing
Neural networks
Structured prediction
Supervised learning by classification

Access to Document

10.1007/978-3-319-77113-7_1

Cite this

Adel, H., Asgari, E., & Schütze, H. (2018). Overview of character-based models for natural language processing. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers (pp. 3-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10761 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-77113-7_1

Adel, Heike ; Asgari, Ehsaneddin ; Schütze, Hinrich. / Overview of character-based models for natural language processing. Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. editor / Alexander Gelbukh. Springer Verlag, 2018. pp. 3-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Character-based models become more and more popular for different natural language processing task, especially due to the success of neural networks. They provide the possibility of directly model text sequences without the need of tokenization and, therefore, enhance the traditional preprocessing pipeline. This paper provides an overview of character-based models for a variety of natural language processing tasks. We group existing work in three categories: tokenization-based approaches, bag-of-n-gram models and end-to-end models. For each category, we present prominent examples of studies with a particular focus on recent character-based deep learning work.",

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author = "Heike Adel and Ehsaneddin Asgari and Hinrich Sch{\"u}tze",

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Adel, H, Asgari, E & Schütze, H 2018, Overview of character-based models for natural language processing. in A Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10761 LNCS, Springer Verlag, pp. 3-16, 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017, Budapest, Hungary, 17/04/17. https://doi.org/10.1007/978-3-319-77113-7_1

Overview of character-based models for natural language processing. / Adel, Heike; Asgari, Ehsaneddin; Schütze, Hinrich.
Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. ed. / Alexander Gelbukh. Springer Verlag, 2018. p. 3-16 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10761 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Adel, Heike

AU - Asgari, Ehsaneddin

AU - Schütze, Hinrich

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

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AB - Character-based models become more and more popular for different natural language processing task, especially due to the success of neural networks. They provide the possibility of directly model text sequences without the need of tokenization and, therefore, enhance the traditional preprocessing pipeline. This paper provides an overview of character-based models for a variety of natural language processing tasks. We group existing work in three categories: tokenization-based approaches, bag-of-n-gram models and end-to-end models. For each category, we present prominent examples of studies with a particular focus on recent character-based deep learning work.

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KW - Feature selection

KW - Language models

KW - Natural language generation

KW - Natural language processing

KW - Neural networks

KW - Structured prediction

KW - Supervised learning by classification

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Adel H, Asgari E, Schütze H. Overview of character-based models for natural language processing. In Gelbukh A, editor, Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. Springer Verlag. 2018. p. 3-16. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-77113-7_1

Overview of character-based models for natural language processing

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