معرفی
Claire Grover is a Senior Research Fellow at the Institute of Language, Cognition and Computation within the School of Informatics at the University of Edinburgh. Her research bridges computational linguistics with real-world applications in public health, urban studies, and digital humanities through advanced Natural Language Processing techniques.
Her primary research interests include Natural Language Processing, Text Mining, Information Extraction, Named Entity Recognition, and Corpus Linguistics. She specializes in developing robust NLP methodologies for domain-specific challenges, particularly in clinical text analysis, historical document processing, and geospatial text mining. Her work emphasizes practical implementation and evaluation of NLP systems for reliable real-world deployment.
Recent publications (2021-2025) demonstrate a clear trend toward interdisciplinary NLP applications: using topic modeling of local news to study neighborhood health effects, evaluating clinical NLP tools for stroke phenotype extraction, and scaling historical text analysis frameworks. These works consistently address methodological reliability and cross-domain adaptability of text analytics systems.
No scientific awards were mentioned in the available information.
Dr. Grover has secured significant research funding as Principal Investigator for projects including ‘Leveraging routinely collected and linked research data to study mental disorders’ (MRC), ‘Empowering elderly people in healthcare conversations’ (EU), and ‘Words on the Street: Digital Literary Cityscape’ (AHRC). Her collaborative network spans computer science, healthcare, and social sciences, with evidence of student supervision through ‘Supervised Work (1)’.
She actively contributes to the Language, Interaction, and Robotics group and Edinburgh Neuroscience initiative, participating in interdisciplinary teams that combine computational expertise with domain knowledge. Her media-covered Trading Consequences project exemplifies her approach to transforming historical trade analysis through text mining.
