Enhancing metabolic event extraction performance with multitask learning concept.

To extract and generate a valid metabolic pathway from research articles, biologists need substantial amounts of time to digest unstructured text. Text mining currently plays a central role in this research area, because it provides the ability to automatically discover useful information in a reasonable time. A text mining model can be built using a training data or a corpus in supervised manner. Unfortunately, a corpus of the domain of interest may not be always available or insufficient in practice, because a corpus construction is a labor-intensive task and needs specialist annotation. In this paper, we developed an event extraction system, a text-mining task, to extract metabolic interactions from research literature and then reconstruct metabolic pathways. The proposed system consists of the pipeline of four supervised-learning steps: named entity recognition, trigger detection, edge detection, and event reconstruction. We also introduced a multitask-learning algorithm, a transfer-learning paradigm, that can leverage additional resources of an existing source domain to facilitate a classification of the metabolic event extraction in the target domain. To demonstrate a proof of concept, edge detection, a core step in our event extraction system, was used as a case study in multitask-learning classification. The experimental results showed that the proposed event extraction system provided competitive performance against those of state-of-the-art related system. In particular, the proposed multitask-learning can improve the performance of edge detection, therefore the overall performance of the event extraction system was also improved accordingly.