Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts
This addresses a specific challenge in biomedical text mining for researchers, but it is incremental as it builds on existing relation extraction methods with new features and a specialized corpus.
The paper tackled the problem of identifying biological context (e.g., species, tissue type) and associating it with biochemical events in biomedical texts, a non-trivial inter-sentential relation extraction task, and presented classifiers trained on syntactic, distance, and frequency features, achieving evaluation results as described in the corpus analysis.
We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type and cell type that are associated with biochemical events. We describe the properties of an annotated corpus of context-event relations and present and evaluate several classifiers for context-event association trained on syntactic, distance and frequency features.