{"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/an-improved-neural-network-model-for-joint","title":"An improved neural network model for joint POS tagging and dependency parsing","arxiv_id":"1807.03955","date":"2018-07-11","proceeding":"CONLL 2018 10","authors":["Dat Quoc Nguyen","Karin Verspoor"],"abstract":"We propose a novel neural network model for joint part-of-speech (POS)\ntagging and dependency parsing. Our model extends the well-known BIST\ngraph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating\na BiLSTM-based tagging component to produce automatically predicted POS tags\nfor the parser. On the benchmark English Penn treebank, our model obtains\nstrong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%\nabsolute improvements to the BIST graph-based parser, and also obtaining a\nstate-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental\nresults on parsing 61 \"big\" Universal Dependencies treebanks from raw texts\nshow that our model outperforms the baseline UDPipe (Straka and Strakov\\'a,\n2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS\nscore. In addition, with our model, we also obtain state-of-the-art downstream\ntask scores for biomedical event extraction and opinion analysis applications.\nOur code is available together with all pre-trained models at:\nhttps://github.com/datquocnguyen/jPTDP","url_abs":"http://arxiv.org/abs/1807.03955v2","url_pdf":"http://arxiv.org/pdf/1807.03955v2.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":"an-improved-neural-network-model-for-joint","repo_url":"https://github.com/datquocnguyen/jPTDP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"event-extraction","task_name":"Event Extraction"},{"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/dependency-parsing-on-penn-treebank","task":"Dependency Parsing","dataset":"Penn Treebank","model":"jPTDP","rank_in_archive_order":15,"of":22,"metrics":{"LAS":"93.87","POS":"97.97","UAS":"95.51"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.03955","atlas_url":"https://app.syntology.ai/?focus=1807.03955","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}