Automated Identification of Discourse Markers Using the NLP Approach: The Case of "Okay"

Autor/inn/en	Sanosi, Abdulaziz; Abdalla, Mohamed
Titel	Automated Identification of Discourse Markers Using the NLP Approach: The Case of "Okay"
Quelle	In: Australian Journal of Applied Linguistics, 4 (2021) 3, S.119-131 (13 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2209-0959
Schlagwörter	Natural Language Processing; Computational Linguistics; Programming Languages; Accuracy; Punctuation; Discourse Analysis; Identification; Evaluators; Comparative Analysis; Computer Software + Suchen Sie Ihr Suchwort? Natürliche Sprache; Linguistics; Computerlinguistik; Interpunktion; Diskursanalyse; Identifikation; Identifizierung
Abstract	This study aimed to examine the potentials of the NLP approach in detecting discourse markers (DMs), namely okay, in transcribed spoken data. One hundred thirty-eight 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 could accurately identify the function of okay as DM or non-DM in 92% of the cases. The inaccuracy of detecting the rest was found to be caused by a lack of proper and detailed punctuations. The main implications of the results are that new NLP approaches can detect DMS; however, proper punctuation is required to enable the proper identification of DMs. In accordance with the findings, the researcher recommended adopting the approach after conducting further comprehensive studies. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01