Knowledge-Poor Context-Sensitive Spelling Correction for Modern Greek

Sagiadinos, Spyridon; Gasteratos, Petros; Dragonas, Vasileios; Kalamara, Athanasia; Spyridonidou, Antonia; Kermanidis, Katia Lida

doi:10.1007/978-3-319-07064-3_29

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Cross-domain Recognition1

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2020

2023

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Cited by 2 publications

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“…Deng et al [79] proposed unsupervised domain adaptation based on autoencoder. The idea was to train the model on a whispered speech from the GeWEC emotion corpus, while normal speech data were used for testing.…”

Section: Cross-domain Recognitionmentioning

confidence: 99%

Human Computer Interaction for Intelligent Systems

2023

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Section: Cross-domain Recognitionmentioning

confidence: 99%

Human Computer Interaction for Intelligent Systems

2023

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Survey of Automatic Spelling Correction

2020

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Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.

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Knowledge-Poor Context-Sensitive Spelling Correction for Modern Greek

Cited by 2 publications

References 9 publications

Human Computer Interaction for Intelligent Systems

Human Computer Interaction for Intelligent Systems

Survey of Automatic Spelling Correction

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