{"id":13486145,"url":"https://github.com/bjascob/amr_coref","last_synced_at":"2026-03-14T05:04:44.386Z","repository":{"id":54603840,"uuid":"344334545","full_name":"bjascob/amr_coref","owner":"bjascob","description":"A python library / model for creating co-references between AMR graph nodes.","archived":false,"fork":false,"pushed_at":"2022-12-11T03:45:51.000Z","size":67,"stargazers_count":9,"open_issues_count":3,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-29T22:11:36.602Z","etag":null,"topics":["abstract-meaning-representation","amr","coreference-resolution","python"],"latest_commit_sha":null,"homepage":"","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/bjascob.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}},"created_at":"2021-03-04T03:12:30.000Z","updated_at":"2024-10-17T10:28:20.000Z","dependencies_parsed_at":"2023-01-26T15:31:31.766Z","dependency_job_id":null,"html_url":"https://github.com/bjascob/amr_coref","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjascob%2Famr_coref","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjascob%2Famr_coref/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjascob%2Famr_coref/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjascob%2Famr_coref/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bjascob","download_url":"https://codeload.github.com/bjascob/amr_coref/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250372715,"owners_count":21419720,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["abstract-meaning-representation","amr","coreference-resolution","python"],"created_at":"2024-07-31T18:00:40.398Z","updated_at":"2026-03-14T05:04:44.320Z","avatar_url":"https://github.com/bjascob.png","language":"Python","funding_links":[],"categories":["Tools","Python"],"sub_categories":[],"readme":"# amr_coref\n\n**A python library / model for creating co-references between AMR graph nodes.**\n\n## About\namr_coref is a python library and trained model designed to do co-referencing\nbetween [Abstract Meaning Representation](https://amr.isi.edu/) graphs.\n\nThe project follows the general approach of the [neuralcoref project](https://github.com/huggingface/neuralcoref)\nand it's excellent\n[blog on the co-referencing](https://medium.com/huggingface/how-to-train-a-neural-coreference-model-neuralcoref-2-7bb30c1abdfe).\nHowever, the model is trained to do direct co-reference resolution between graph nodes and does not depend on\nthe sentences the graphs were created from.\n\nThe trained model achieves the following scores\n```\nMUC   :  R=0.647  P=0.779  F₁=0.706\nB³    :  R=0.633  P=0.638  F₁=0.630\nCEAF_m:  R=0.515  P=0.744  F₁=0.609\nCEAF_e:  R=0.200  P=0.734  F₁=0.306\nBLANC :  R=0.524  P=0.799  F₁=0.542\nCoNLL-2012 average score: 0.548\n```\n\n## Project Status\n**!! The following papers have GitHub projects/code that are better scoring and may be a preferable solution.**\nSee the uploaded file in [#1](https://github.com/bjascob/amr_coref/issues/1) for a quick view of scores.\n* [VGAE as Cheap Supervision for AMR Coreference Resolution](https://github.com/IreneZihuiLi/VG-AMRCoref)\n* [End-to-end AMR Coreference Resolution](https://github.com/Sean-Blank/AMRcoref)\n\nNote that due to the use of multiprocessing, this code may only be compatible with a Debian style OS.\nSee [#3](https://github.com/bjascob/amr_coref/issues/3) for details on the issue.\n\n\n## Installation and usage\nThere is currently no pip installation. To use the library, simply clone the code and use it in place.\n\nThe pre-trained model can be downloaded from the assets section in [releases](https://github.com/bjascob/amr_coref/releases).\n\nTo use the model create a `data` directory and un-tar the model in it.\n\nThe script `40_Run_Inference.py`, is an example of how to use the model.\n\n\n## Training\nIf you'd like to train the model from scratch, you'll need a copy of the\n[AMR corpus](https://catalog.ldc.upenn.edu/LDC2020T02).\nTo complete training, run the scripts in order.\n- 10_Build_Model_TData.py\n- 12_Build_Embeddings.py\n- 14_Build_Mention_Tokens.py\n- 30_Train_Model.py.\n\nYou'll need `amr_annotation_3.0` and `GloVe/glove.6B.50d.txt` in your `data` directory\n\nThe first few scripts will create the training data in `data/tdata` and the model training\nscript will create `data/model`. Training takes less than 4 hours.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjascob%2Famr_coref","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbjascob%2Famr_coref","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjascob%2Famr_coref/lists"}