# Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts

## TL;DR

This paper addresses the challenge of extracting inter-sentential biological context relations, such as species and tissue type, associated with biochemical events in biomedical texts, using classifiers trained on syntactic and contextual features.

## Contribution

It introduces an annotated corpus for context-event relations and evaluates classifiers for associating biological context with biochemical events across sentences.

## Key findings

- Classifiers achieve promising accuracy in context-event association.
- Syntactic and distance features improve extraction performance.
- Annotated corpus facilitates future research in biomedical relation extraction.

## Abstract

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.