{"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/improving-coverage-and-runtime-complexity-for","title":"Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers","arxiv_id":"1804.10615","date":"2018-04-27","proceeding":"NAACL 2018 6","authors":["Tianze Shi","Carlos Gómez-Rodríguez","Lillian Lee"],"abstract":"We generalize Cohen, G\\'omez-Rodr\\'iguez, and Satta's (2011) parser to a\nfamily of non-projective transition-based dependency parsers allowing\npolynomial-time exact inference. This includes novel parsers with better\ncoverage than Cohen et al. (2011), and even a variant that reduces time\ncomplexity to $O(n^6)$, improving over the known bounds in exact inference for\nnon-projective transition-based parsing. We hope that this piece of theoretical\nwork inspires design of novel transition systems with better coverage and\nbetter run-time guarantees.\n  Code available at https://github.com/tzshi/nonproj-dp-variants-naacl2018","url_abs":"http://arxiv.org/abs/1804.10615v2","url_pdf":"http://arxiv.org/pdf/1804.10615v2.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":"improving-coverage-and-runtime-complexity-for","repo_url":"https://github.com/tzshi/nonproj-dp-variants-naacl2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}