Chemical –protein interaction extraction via contextualized word representations and multihead attention

We present a deep neural model for CPI extraction based on deep c ontext representation and multihead attention. Our model mainly consists of the following three parts: a deep context representation layer, a bidirectional long short-term memory networks (Bi-LSTMs) layer and a multihead attention layer. The deep context representation is employed to provide more co mprehensive feature input for Bi-LSTMs. The multihead attention can effectively emphasize the important part of the Bi-LSTMs output. We evaluated our method on the public ChemProt corpus. These experimental results show that both deep context representation and multihead attention are helpful in CPI extraction. Our method can compete with other state-of-the-art methods on ChemProt corpus.
Source: Database : The Journal of Biological Databases and Curation - Category: Databases & Libraries Source Type: research