Attention MeSH: Simple, Effective and Interpretable Automatic MeSH Indexer
Jin Q, Dhingra B, Cohen WW, Lu X. Attention MeSH: Simple, Effective and Interpretable Automatic MeSH Indexer.
There are millions of articles in PubMed database. To facilitate information retrieval, curators in the National Library of Medicine (NLM) assign a set of Medical Subject Head-ings (MeSH) to each article. MeSH is a hierarchically-organized vocabulary, contain-ing about 28K different concepts, covering the fields from clinical medicine to informa-tion sciences. Several automatic MeSH index-ing models have been developed to improve the time-consuming and financially expensive manual annotation, including the NLM official tool – Medical Text Indexer, and the winner of BioASQ Task5a challenge – DeepMeSH. However, these models are complex and not interpretable. We propose a novel end-to-end model, AttentionMeSH, which utilizes deep learning and attention mechanism to index MeSH terms to biomedical text. The attention mechanism enables the model to associate tex-tual evidence with annotations, thus providing interpretability at the word level. The model also uses a novel masking mechanism to en-hance accuracy and speed. In the final week of BioASQ Chanllenge Task6a, we ranked 2nd by average MiF using an on-construction model. After the contest, we achieve close to state-of-the-art MiF performance of ∼ 0.684 using our final model. Human evaluations show AttentionMeSH also provides high level of interpretability, retrieving about 90% of all expert-labeled relevant words given an MeSH-article pair at 20 output.
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