Temporal Expression Recognition for Cell Cycle Phase Concepts in Biomedical Literature

Abstract

In this paper, we present a system for recognizing temporal expressions related to cell cycle phase (CCP) concepts in biomedical literature. We identified 11 classes of cell cycle related temporal expressions, for which we made extensions to TIMEX3, arranging them in an ontology derived from the Gene Ontology. We annotated 310 abstracts from PubMed. Annotation guidelines were developed, consistent with existing time-related annotation guidelines for TimeML. Two annotators participated in the annotation. We achieved an inter-annotator agreement of 0.79 for an exact span match and 0.82 for relaxed constraints. Our approach is a hybrid of machine learning to recognize temporal expressions and a rule-based approach to map them to the ontology. We trained a named entity recognizer using Conditional Random Fields (CRF) models. An off-the-shelf implementation of the linear chain CRF model was used. We obtained an F-score of 0.77 for temporal expression recognition. We achieved 0.79 macro-averagee F-score and 0.78 micro-averaged F-score for mapping to the ontology.