Analysing adjectives used in a histopathology corpus with NLP tools

Our work deals with the domain of hispathology. In diagnosis histopathological images are described differently by different observers and even by the same observer at different times. This divergence in the identification of specific morphological features is partly due to varying levels of expertise among pathologists and to differences in subjective analysis and comprehension of pathological images. As linguists and developers of Natural Language Processing (NLP) systems, we started a collaboration with the Medical Informatics Department at the Broussais Hospital in order to explore a new method for corpus-based medical glossary acquisition. We focused our analysis on adjectives because they are the main linguistic category involved in the evaluation process. The first results of this study show the relevance of a corpus-based approach to cope with the “subjective” interpretations given by pathologists when they analyse microscopic images.