{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/experiment-segmentation-in-scientific","title":"Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks","arxiv_id":"1702.05398","date":"2017-02-17","proceeding":null,"authors":["Pradeep Dasigi","Gully A. P. C. Burns","Eduard Hovy","Anita de Waard"],"abstract":"We propose a deep learning model for identifying structure within experiment\nnarratives in scientific literature. We take a sequence labeling approach to\nthis problem, and label clauses within experiment narratives to identify the\ndifferent parts of the experiment. Our dataset consists of paragraphs taken\nfrom open access PubMed papers labeled with rhetorical information as a result\nof our pilot annotation. Our model is a Recurrent Neural Network (RNN) with\nLong Short-Term Memory (LSTM) cells that labels clauses. The clause\nrepresentations are computed by combining word representations using a novel\nattention mechanism that involves a separate RNN. We compare this model against\nLSTMs where the input layer has simple or no attention and a feature rich CRF\nmodel. Furthermore, we describe how our work could be useful for information\nextraction from scientific literature.","url_abs":"http://arxiv.org/abs/1702.05398v1","url_pdf":"http://arxiv.org/pdf/1702.05398v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"experiment-segmentation-in-scientific","repo_url":"https://github.com/edvisees/sciDT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"experiment-segmentation-in-scientific","repo_url":"https://github.com/SciKnowEngine/Molecular_Interaction_Evidence_Fragment_Corpus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}