Linguistic Annotation

Linguistic annotation involves tagging text or speech data with various linguistic features, such as part-of-speech, syntactic structure, semantic roles, and pragmatic information, to facilitate computational linguistic analysis and improve natural language processing (NLP) applications. Current research focuses on developing efficient annotation methods, including manual annotation, rule-based systems, and machine learning approaches leveraging large language models (LLMs) and deep learning architectures to automate the process, particularly for under-resourced languages. The creation of high-quality annotated corpora is crucial for advancing linguistic research, improving NLP tools across diverse languages, and addressing challenges like hate speech detection and cross-cultural communication.