Volume 4, Issue 3 December 2021, pp. 119–131
Automated identification of discourse markers using NLP approach: The case of okay
This study aimed to examine the potentials of the NLP approach in detecting discourse markers (DMs), namely okay, in a transcribed spoken data. A total of 138 concordance lines were presented to human referees to judge the functions of okay in them as a DM or non-DM. After that, the researchers used a Python script written according to the POS tagging scheme of the NLTK library to set rules for identifying cases where okay is used as non-DM. The output of the script was compared to the reference human-annotated data. The results showed that the script was capable of identifying the function of okay as DM or non-DM accurately in 92% of the cases. The inaccuracy of detecting the rest was found to be caused by a lack of proper and detailed punctuation. The implications of the results are discussed, and relevant recommendations are presented by the researchers.
© Abdulaziz Sanosi, Mohamed Abdalla
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Sanosi, A., & Abdalla, A. (2021). Automated identification of discourse markers using NLP approach: The case of okay. Australian Journal of Applied Linguistics, 4(3), 119–131. https://doi.org/10.29140/ajal.v4n3.538
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