Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
This work addresses the need for better structured prediction in medical abstract analysis, though it is incremental as it builds on existing neural network approaches.
The paper tackles the problem of sequential sentence classification in medical abstracts by proposing a hierarchical neural network that uses contextual information from surrounding sentences, achieving a 2%-3% improvement over state-of-the-art results on two benchmark datasets.
Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.