{"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/a-novel-neural-network-model-for-joint-pos","title":"A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing","arxiv_id":"1705.05952","date":"2017-05-16","proceeding":"CONLL 2017 8","authors":["Dat Quoc Nguyen","Mark Dras","Mark Johnson"],"abstract":"We present a novel neural network model that learns POS tagging and\ngraph-based dependency parsing jointly. Our model uses bidirectional LSTMs to\nlearn feature representations shared for both POS tagging and dependency\nparsing tasks, thus handling the feature-engineering problem. Our extensive\nexperiments, on 19 languages from the Universal Dependencies project, show that\nour model outperforms the state-of-the-art neural network-based\nStack-propagation model for joint POS tagging and transition-based dependency\nparsing, resulting in a new state of the art. Our code is open-source and\navailable together with pre-trained models at:\nhttps://github.com/datquocnguyen/jPTDP","url_abs":"http://arxiv.org/abs/1705.05952v2","url_pdf":"http://arxiv.org/pdf/1705.05952v2.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":"a-novel-neural-network-model-for-joint-pos","repo_url":"https://github.com/datquocnguyen/jPTDP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"pos","task_name":"POS"},{"task_slug":"pos-tagging","task_name":"POS Tagging"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"transition-based-dependency-parsing","task_name":"Transition-Based Dependency Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/part-of-speech-tagging-on-ud","task":"Part-Of-Speech Tagging","dataset":"UD","model":"Joint Bi-LSTM","rank_in_archive_order":5,"of":5,"metrics":{"Avg accuracy":"95.55"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.05952","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}