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Grammar checking and relation extraction in text: approaches, techniques and open challenges

Nora Madi (Department of Information Technology, King Saud University, Riyadh, Saudi Arabia)
Rawan Al-Matham (Department of Information Technology, King Saud University, Riyadh, Saudi Arabia)
Hend Al-Khalifa (Department of Information Technology, King Saud University, Riyadh, Saudi Arabia)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 14 June 2019

Issue publication date: 2 September 2019

Abstract

Purpose

The purpose of this paper is to provide an overall review of grammar checking and relation extraction (RE) literature, their techniques and the open challenges associated with them; and, finally, suggest future directions.

Design/methodology/approach

The review on grammar checking and RE was carried out using the following protocol: we prepared research questions, planed for searching strategy, addressed paper selection criteria to distinguish relevant works, extracted data from these works, and finally, analyzed and synthesized the data.

Findings

The output of error detection models could be used for creating a profile of a certain writer. Such profiles can be used for author identification, native language identification or even the level of education, to name a few. The automatic extraction of relations could be used to build or complete electronic lexical thesauri and knowledge bases.

Originality/value

Grammar checking is the process of detecting and sometimes correcting erroneous words in the text, while RE is the process of detecting and categorizing predefined relationships between entities or words that were identified in the text. The authors found that the most obvious challenge is the lack of data sets, especially for low-resource languages. Also, the lack of unified evaluation methods hinders the ability to compare results.

Keywords

Citation

Madi, N., Al-Matham, R. and Al-Khalifa, H. (2019), "Grammar checking and relation extraction in text: approaches, techniques and open challenges", Data Technologies and Applications, Vol. 53 No. 3, pp. 373-394. https://doi.org/10.1108/DTA-01-2019-0001

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited