Building custom NLP tools to annotate discourse-functional features for second language writing research: A tutorial

The current tutorial paper describes a process of developing a custom natural language processing model with a particular focus on a discourse annotation task. After an overview of recent developments in natural language processing (NLP), the paper discusses the development of the Engagement Analyze...

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Bibliographic Details
Published in:Research Methods in Applied Linguistics 2024-12, Vol.3 (3), p.100153, Article 100153
Main Authors: Eguchi, Masaki, Kyle, Kristopher
Format: Article
Language:English
Subjects:
Appraisal analysis
Corpus annotation
Epistemic stance
L2 writing
Natural language processing
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Summary:The current tutorial paper describes a process of developing a custom natural language processing model with a particular focus on a discourse annotation task. After an overview of recent developments in natural language processing (NLP), the paper discusses the development of the Engagement Analyzer (Eguchi & Kyle, 2023), focusing on corpus annotation, the machine learning model, model training, evaluation, and dissemination. A step-by-step tutorial of this process via the spaCy Python package is provided. The paper highlights the feasibility of developing custom NLP tools to enhance the scalability and replicability of the annotation of context-sensitive linguistic features in L2 writing research.
ISSN:2772-7661
2772-7661
DOI:10.1016/j.rmal.2024.100153