The Journal of Advanced Corpus-Oriented Research (JACOR) aims to foster high-quality research in corpus linguistics and its allied disciplines, offering a dedicated platform for the exploration of language through empirical, data-driven, and computational methods. The journal is committed to advancing theoretical, methodological, and applied aspects of corpus-based research, and encourages interdisciplinary approaches that bridge linguistics with technology, education, and the digital humanities.
Aims:
To promote innovative research in corpus linguistics, computational linguistics, and natural language processing.
To encourage the development and application of corpus tools and technologies in linguistic inquiry.
To support the integration of artificial intelligence and machine learning in corpus analysis.
To contribute to language teaching and learning through corpus-based pedagogical approaches.
To provide a forum for emerging scholars, especially from underrepresented linguistic and regional backgrounds.
To disseminate knowledge that enhances the study of language variation, change, and use in diverse contexts.
Scope:
JACOR welcomes contributions that include, but are not limited to:
Corpus Linguistics: corpus design, annotation, analysis, multimodal corpora, lexicography, sociolinguistic and historical studies using corpora.
Computational Linguistics: tool development, computational morphology, semantic processing, and cross-linguistic analysis using corpora.
Natural Language Processing (NLP): applications in sentiment analysis, speech processing, low-resource languages, and AI-based linguistic modeling.
Language Teaching and Digital Humanities: corpus-assisted pedagogy, educational technologies, and digital methodologies for language research.
AI in Linguistic Research: implementation of large language models, neural machine translation, and ethical issues surrounding AI in language studies.
General and Applied Linguistics: discourse analysis, pragmatics, forensic linguistics, translation studies, and language policy explored through corpus methods.