A Novel Serial Deep Multi-Task Learning Model For Large Scale Biomedical Semantic Indexing

Biomedical semantic indexing refers to annotating biomedical citations with Medical Subject Headings, which is crucial for texting mining, information retrieval and other researches in the field of bioinformatics. The traditional methods ignore the relations among labels and need complicated feature engineering. In this paper, we present a novel model with a deep serial multi-task learning structure, in which the semantic word embedding and bidirectional Gated Recurrent Unit are integrated in a multi-task learning paradigm. It differs from an ordinary multi-task structure in that the tasks in our model are serial and tightly coupled rather than parallel. The dataset of the 2017 BioASQ-Task5A is used to evaluate the performance. Without any handcrafted feature, our model outperforms MTI, the state-of-the-art solution proposed by the US National Library of Medicine. It also achieves the highest precision among all the solutions in 2017 BioASQ-Task5A, and converges faster than some naive deep learning methods.