Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
This work addresses the need for improved sentence classification in medical abstracts, which is incremental as it builds on existing neural network and structured prediction methods.
The authors tackled the problem of sentence classification in medical abstracts by developing a neural network architecture that combines isolated sentence classification with structured prediction, achieving state-of-the-art results on two datasets.
Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model achieves state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.