{"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/combining-second-order-graph-based-and-headed","title":"Combining (second-order) graph-based and headed-span-based projective dependency parsing","arxiv_id":"2108.05838","date":"2021-08-12","proceeding":"Findings (ACL) 2022 5","authors":["Songlin Yang","Kewei Tu"],"abstract":"Graph-based methods, which decompose the score of a dependency tree into scores of dependency arcs, are popular in dependency parsing for decades. Recently, \\citet{Yang2022Span} propose a headed-span-based method that decomposes the score of a dependency tree into scores of headed spans. They show improvement over first-order graph-based methods. However, their method does not score dependency arcs at all, and dependency arcs are implicitly induced by their cubic-time algorithm, which is possibly sub-optimal since modeling dependency arcs is intuitively useful. In this work, we aim to combine graph-based and headed-span-based methods, incorporating both arc scores and headed span scores into our model. First, we show a direct way to combine with $O(n^4)$ parsing complexity. To decrease complexity, inspired by the classical head-splitting trick, we show two $O(n^3)$ dynamic programming algorithms to combine first- and second-order graph-based and headed-span-based methods. Our experiments on PTB, CTB, and UD show that combining first-order graph-based and headed-span-based methods is effective. We also confirm the effectiveness of second-order graph-based parsing in the deep learning age, however, we observe marginal or no improvement when combining second-order graph-based and headed-span-based methods. Our code is publicly available at \\url{https://github.com/sustcsonglin/span-based-dependency-parsing}.","url_abs":"https://arxiv.org/abs/2108.05838v2","url_pdf":"https://arxiv.org/pdf/2108.05838v2.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":"combining-second-order-graph-based-and-headed","repo_url":"https://github.com/sustcsonglin/span-based-dependency-parsing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"arc","task_name":"ARC"},{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.05838","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}