Using Neural Networks for Relation Extraction from Biomedical Literature
This work addresses the need for automated relation extraction in biomedical research, though it appears incremental by building on existing neural network methods.
The paper tackles the problem of extracting relations between biomedical concepts from literature by using neural networks, particularly multichannel architectures that incorporate biomedical ontologies, leading to state-of-the-art results.
Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.