{"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/collective-entity-disambiguation-with","title":"Collective Entity Disambiguation with Structured Gradient Tree Boosting","arxiv_id":"1802.10229","date":"2018-02-28","proceeding":"NAACL 2018 6","authors":["Yi Yang","Ozan .Irsoy","Kazi Shefaet Rahman"],"abstract":"We present a gradient-tree-boosting-based structured learning model for\njointly disambiguating named entities in a document. Gradient tree boosting is\na widely used machine learning algorithm that underlies many top-performing\nnatural language processing systems. Surprisingly, most works limit the use of\ngradient tree boosting as a tool for regular classification or regression\nproblems, despite the structured nature of language. To the best of our\nknowledge, our work is the first one that employs the structured gradient tree\nboosting (SGTB) algorithm for collective entity disambiguation. By defining\nglobal features over previous disambiguation decisions and jointly modeling\nthem with local features, our system is able to produce globally optimized\nentity assignments for mentions in a document. Exact inference is prohibitively\nexpensive for our globally normalized model. To solve this problem, we propose\nBidirectional Beam Search with Gold path (BiBSG), an approximate inference\nalgorithm that is a variant of the standard beam search algorithm. BiBSG makes\nuse of global information from both past and future to perform better local\nsearch. Experiments on standard benchmark datasets show that SGTB significantly\nimproves upon published results. Specifically, SGTB outperforms the previous\nstate-of-the-art neural system by near 1\\% absolute accuracy on the popular\nAIDA-CoNLL dataset.","url_abs":"http://arxiv.org/abs/1802.10229v2","url_pdf":"http://arxiv.org/pdf/1802.10229v2.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":"collective-entity-disambiguation-with","repo_url":"https://github.com/bloomberg/sgtb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-disambiguation","task_name":"Entity Disambiguation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1802.10229","atlas_url":"https://app.syntology.ai/?focus=1802.10229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10229"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/bloomberg/sgtb","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"3641147851e7dcf1","entry":"make_idx_data","repo":"bloomberg/sgtb","repo_kind":"official","path":"structured_learner.py","file_url":"https://github.com/bloomberg/sgtb/blob/HEAD/structured_learner.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3641147851e7dcf1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}