nCoder+: A Semantic Tool for Improving Recall of nCoder Coding

Autor/inn/en	Cai, Zhiqiang; Siebert-Evenstone, Amanda; Eagan, Brendan; Shaffer, David Williamson; Hu, Xiangen; Graesser, Arthur C.
Titel	nCoder+: A Semantic Tool for Improving Recall of nCoder Coding
Quelle	(2019), (14 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	ORCID (Cai, Zhiqiang) ORCID (Siebert-Evenstone, Amanda) ORCID (Eagan, Brendan) ORCID (Shaffer, David Williamson) ORCID (Hu, Xiangen) ORCID (Graesser, Arthur C.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Semantics; Computational Linguistics; Evidence; Coding; Integrated Learning Systems; Computer Software; Classification; Reliability; Validity; Natural Language Processing; Error Patterns; Recall (Psychology); Sample Size; Information Retrieval + Suchen Sie Ihr Suchwort? Semantik; Linguistics; Computerlinguistik; Evidenz; Codierung; Programmierung; Classification system; Klassifikation; Klassifikationssystem; Reliabilität; Gültigkeit; Natürliche Sprache; Fehlertyp; Abberufung
Abstract	Coding is a process of assigning meaning to a given piece of evidence. Evidence may be found in a variety of data types, including documents, research interviews, posts from social media, conversations from learning platforms, or any source of data that may provide insights for the questions under qualitative study. In this study, we focus on text data and consider coding as a process of identifying words or phrases and categorizing them into codes to facilitate data analysis. There are a number of different approaches to generating qualitative codes, such as grounded coding, a priori coding, or using both in an iterative process. However, both qualitative and quantitative analysts face the same coding problem: when the data size is large, manually coding becomes impractical. nCoder is a tool that helps researchers to discover and code key concepts in text data with minimum human judgements. Once reliability and validity are established, nCoder automatically applies the coding scheme to the dataset. However, for concepts that occur infrequently, even with an acceptable reliability, the classifier may still result in too many false negatives. This paper explores these problems within the current nCoder and proposes adding a semantic component to the nCoder. A tool called "nCoder+" is presented with real data to demonstrate the usefulness of the semantic component. The possible ways of integrating this component and other natural language processing techniques into nCoder are discussed. [This paper was published in: "ICQE 2019," CCIS 1112, edited by B. Eagen et al., Springer, 2019, pp. 41-54.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01