{"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/hierarchical-neural-networks-for-sequential","title":"Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts","arxiv_id":"1808.06161","date":"2018-08-19","proceeding":"EMNLP 2018 10","authors":["Di Jin","Peter Szolovits"],"abstract":"Prevalent models based on artificial neural network (ANN) for sentence\nclassification often classify sentences in isolation without considering the\ncontext in which sentences appear. This hampers the traditional sentence\nclassification approaches to the problem of sequential sentence classification,\nwhere structured prediction is needed for better overall classification\nperformance. In this work, we present a hierarchical sequential labeling\nnetwork to make use of the contextual information within surrounding sentences\nto help classify the current sentence. Our model outperforms the\nstate-of-the-art results by 2%-3% on two benchmarking datasets for sequential\nsentence classification in medical scientific abstracts.","url_abs":"http://arxiv.org/abs/1808.06161v1","url_pdf":"http://arxiv.org/pdf/1808.06161v1.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":"hierarchical-neural-networks-for-sequential","repo_url":"https://github.com/jind11/HSLN-Joint-Sentence-Classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-classification","task_name":"Sentence Classification"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentence-classification-on-pubmed-20k-rct","task":"Sentence Classification","dataset":"PubMed 20k RCT","model":"Hierarchical Neural Networks","rank_in_archive_order":1,"of":2,"metrics":{"F1":"92.60"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}