Multi-task Learning Applied to Biomedical Named Entity Recognition Task

Recent deep learning techniques have shown significant improvements in biomedical named entity recognition task. However, such techniques are still facing challenges; one of them is related to the limited availability of annotated text data. In this perspective, with a multi-task approach, simultaneously training different related tasks enables multi-task models to learn common features among different tasks where they share some layers with each other. It is desirable to used stacked long-short term memories (LSTMs) in such models to deal with a large amount of training data and to learn the underlying hidden structure in the data. However, the stacked LSTMs approach also leads to the vanishing gradient problem. To alleviate this limitation, we propose a multi-task model based on convolution neural networks, stacked LSTMs, and conditional random fields and use embedding information at different layers. The model proposed shows results comparable to state-of-the-art approaches. Moreover, we performed an empirical analysis of the proposed model with different variations to see their impact on our model.