{"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/amr-parsing-using-stack-lstms","title":"AMR Parsing using Stack-LSTMs","arxiv_id":"1707.07755","date":"2017-07-24","proceeding":"EMNLP 2017 9","authors":["Miguel Ballesteros","Yaser Al-Onaizan"],"abstract":"We present a transition-based AMR parser that directly generates AMR parses\nfrom plain text. We use Stack-LSTMs to represent our parser state and make\ndecisions greedily. In our experiments, we show that our parser achieves very\ncompetitive scores on English using only AMR training data. Adding additional\ninformation, such as POS tags and dependency trees, improves the results\nfurther.","url_abs":"http://arxiv.org/abs/1707.07755v2","url_pdf":"http://arxiv.org/pdf/1707.07755v2.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":[],"tasks":[{"task_slug":"amr-parsing","task_name":"AMR Parsing"},{"task_slug":"pos","task_name":"POS"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/amr-parsing-on-ldc2014t12-1","task":"AMR Parsing","dataset":"LDC2014T12","model":"Transition-based parser-Stack-LSTM","rank_in_archive_order":11,"of":12,"metrics":{"F1 Full":"63","F1 Newswire":"68"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.07755","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}