{"id":23462568,"url":"https://github.com/dnanhkhoa/bert-span-parser","last_synced_at":"2026-04-30T15:31:07.672Z","repository":{"id":162585181,"uuid":"180982685","full_name":"dnanhkhoa/bert-span-parser","owner":"dnanhkhoa","description":null,"archived":false,"fork":false,"pushed_at":"2020-06-04T05:43:30.000Z","size":4107,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-12T09:48:14.313Z","etag":null,"topics":["bert","constituency-parser","nlp","pytorch","vietnamese"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dnanhkhoa.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-04-12T10:06:17.000Z","updated_at":"2020-06-04T09:07:37.000Z","dependencies_parsed_at":null,"dependency_job_id":"1a9b977f-ad22-426e-ac0a-787d80b5cad7","html_url":"https://github.com/dnanhkhoa/bert-span-parser","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dnanhkhoa/bert-span-parser","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnanhkhoa%2Fbert-span-parser","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnanhkhoa%2Fbert-span-parser/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnanhkhoa%2Fbert-span-parser/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnanhkhoa%2Fbert-span-parser/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dnanhkhoa","download_url":"https://codeload.github.com/dnanhkhoa/bert-span-parser/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dnanhkhoa%2Fbert-span-parser/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32469344,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-30T13:12:12.517Z","status":"ssl_error","status_checked_at":"2026-04-30T13:12:06.837Z","response_time":57,"last_error":"SSL_read: 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experiments on the well-known and publicly available Vietnamese Treebank [corpus](https://link.springer.com/article/10.1007/s10579-015-9308-5?shared-article-renderer) from the VLSP project with bracketed structure format (similar to English Penn Treebank).\n\nConstructing train/valid/test sets with 18:1:1 ratio.\n\n### Evaluation Metric\n\n\u003cimg src=\"https://render.githubusercontent.com/render/math?math=Precision = \\frac{|T \\cap T^*|}{|T|}\"\u003e\n\n\u003cimg src=\"https://render.githubusercontent.com/render/math?math=Recall = \\frac{|T \\cap T^*|}{|T^*|}\"\u003e\n\n\u003cimg src=\"https://render.githubusercontent.com/render/math?math=F1 = \\frac{2 \\times Precision \\times Recall}{Precision\\ %2B\\ Recall}\"\u003e\n\nWhere `T` is predicted tree (\u003cimg src=\"https://render.githubusercontent.com/render/math?math=T := ((i_t, j_t), l_t): 0 \\leq t \\leq |T|\"\u003e, where \u003cimg src=\"https://render.githubusercontent.com/render/math?math=(i_t, j_t)\"\u003e is a span and \u003cimg src=\"https://render.githubusercontent.com/render/math?math=l_t\"\u003e is the corresponding label)  and `T*` is golden tree.\n\n### Results\n\n#### Vietnamese Treebank\n\n| Model                       | F1 Dev | F1 Test                    |\n|-----------------------------|--------|----------------------------|\n| Shift-reduce parser (J.Cross \u0026 L.Huang, 2016) [1]| 67.47 | 68.65 (R: 64.24, P: 73.70) |\n| Beam parser (J.Hong \u0026 L.Huang, 2018) [2]| 75.02 | 74.84 (R: 75.47, P: 74.22) |\n| Minimal top-down parser (M.Stern et al., 2017) [3]| 74.18 | 74.36 (R: 74.02, P: 74.70) |\n| Minimal chart parser (M.Stern et al., 2017) [3]| 75.24 | 76.14 (R: 75.04, P: 77.27) |\n| Fasttext minimal parser (our test) | 75.35 | 76.30 (R: 76.45, P: 76.14) |\n| Using BERT as feature (our work)  [4]     | 79.49  | 79.82 (R: 78.29; P: 81.41) |\n| Using BERT with fine-tuning (our work) [4]| 80.95  | 81.29 (R: 80.47, P: 82.12) |\n\n#### References\n[1] James Cross \u0026 Liang Huang, Span-based constituency parsing with a structure-label system and provably optimal dynamic oracles, EMNLP, 2016.\n\n[2] Juneki Hong \u0026 Liang Huang, Linear-Time Constituency Parsing with RNNs and Dynamic Programming, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Short Papers), trang 477-483, Melbourne, Australia, 15-20/07/2018.\n\n[3] Mitchell Stern, Jacob Andreas, Dan Klein, A minimal span-based neural constituency parser, In Proceedings of the Association for Computational Linguistics, 2017a.\n\n[4] Thi-Phuong-Uyen PHAN, Ngoc-Thanh-Tung HUYNH, Hung-Thinh TRUONG, Tuan-An DAO, Dien Dinh, Vietnamese Span-based Constituency Parsing with BERT Embedding, 2019, 1-7. 10.1109/KSE.2019.8919467. 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