2017/02/17 by Pradeep Dasigi, Gully A. P. C. Burns, Dasigi, Pradeep +6 · 22 citations
Computer Science · #Advanced Text Analysis Techniques #Annotation #Artificial intelligence #Artificial neural network #Computation and Language (cs.CL) #Computer science #Deep learning #FOS: Computer and information sciences #Feature (linguistics) #Feature engineering #Layer (electronics) #Linguistics #Narrative #Natural Language Processing Techniques #Natural language processing #Recurrent neural network #Rhetorical question #Segmentation #Sequence labeling #Task (project management) #Topic Modeling #Word (group theory) #cs.CL
paper · pdf · doi:10.48550/arxiv.1702.05398
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/02/17 · openalex publication_date 2017/02/17 · arxiv updated 2017/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a deep learning model for identifying structure within experiment narratives in scientific literature. We take a sequence labeling approach to this problem, and label clauses within experiment narratives to identify the different parts of the experiment. Our dataset consists of paragraphs taken from open access PubMed papers labeled with rhetorical information as a result of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells that labels clauses. The clause representations are computed by combining word representations using a novel attention mechanism that involves a separate RNN. We compare this model against LSTMs where the input layer has simple or no attention and a feature rich CRF model. Furthermore, we describe how our work could be useful for information extraction from scientific literature.