Hierarchical bi-directional attention-based RNNs for supporting document classification on protein-protein interactions affected by genetic mutations.

In this paper, we describe a hierarchical bi-directional attention-based Re-current Neural Network (RNN) as a reusable sequence encoder architecture, which is used as sentence and document encoder for document classification. The sequence encoder is composed of two bi-directional RNN equipped with an attention mechanism that identifies and captures the most important elements, words or sentences, in a document followed by a dense layer for the classification task. Our approach utilizes the hierarchical nature of documents which are composed of sequences of sentences and sentences are composed of sequences of words.