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2018 | OriginalPaper | Buchkapitel

Overview of Character-Based Models for Natural Language Processing

verfasst von : Heike Adel, Ehsaneddin Asgari, Hinrich Schütze

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

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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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Nächstes Kapitel Pooling Word Vector Representations Across Models
Fußnoten
1
There are also difficult cases in English, such as “Yahoo!” or “San Francisco-Los Angeles flights”.
 
2
In our view, morpheme-based models are not true instances of character-level models as linguistically motivated morphological segmentation is an equivalent step to tokenization, but on a different level. We therefore do not cover most work on morphological segmentation in this paper.
 
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Metadaten
Titel
Overview of Character-Based Models for Natural Language Processing
verfasst von
Heike Adel
Ehsaneddin Asgari
Hinrich Schütze
Copyright-Jahr
2018
Buch
Computational Linguistics and Intelligent Text Processing

Print ISBN: 978-3-319-77112-0

Electronic ISBN: 978-3-319-77113-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77113-7_1

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