Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

TL;DR

This paper introduces a neural network architecture that jointly classifies sentences in medical paper abstracts, leveraging structured prediction to improve accuracy over traditional models that classify sentences independently.

Contribution

The authors propose a novel ANN model that combines sentence classification with structured prediction, enhancing performance in sequential sentence classification tasks.

Findings

Achieved state-of-the-art results on two medical abstract datasets.

Outperformed models that classify sentences independently.

Demonstrated the effectiveness of combining ANN with structured prediction.

Abstract

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.