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We propose a deep learning model for identifying structure within experimentnarratives in scientific literature. We take a sequence labeling approach tothis problem, and label clauses within experiment narratives to identify thedifferent parts of the experiment. Our dataset consists of paragraphs takenfrom open access PubMed papers labeled with rhetorical information as a resultof our pilot annotation. Our model is a Recurrent Neural Network (RNN) withLong Short-Term Memory (LSTM) cells that labels clauses. The clauserepresentations are computed by combining word representations using a novelattention mechanism that involves a separate RNN. We compare this model againstLSTMs where the input layer has simple or no attention and a feature rich CRFmodel. Furthermore, we describe how our work could be useful for informationextraction from scientific literature.

Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks