{"id":20772107,"url":"https://github.com/gyorilab/adeft","last_synced_at":"2025-04-30T14:27:13.425Z","repository":{"id":34911213,"uuid":"156276061","full_name":"gyorilab/adeft","owner":"gyorilab","description":"Tool for disambiguating acronyms and abbreviations in text for NLP applications","archived":false,"fork":false,"pushed_at":"2024-06-12T17:32:23.000Z","size":11604,"stargazers_count":22,"open_issues_count":1,"forks_count":10,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-30T14:27:06.389Z","etag":null,"topics":["acronym-disambiguation"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gyorilab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","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":"2018-11-05T20:08:03.000Z","updated_at":"2025-02-25T00:30:22.000Z","dependencies_parsed_at":"2024-05-09T04:27:30.022Z","dependency_job_id":"dcdf4794-cc87-45ba-a6cc-2bf9c75730bf","html_url":"https://github.com/gyorilab/adeft","commit_stats":{"total_commits":930,"total_committers":9,"mean_commits":"103.33333333333333","dds":"0.15053763440860213","last_synced_commit":"cbb643d6d4b8378931292295743c67d285419cab"},"previous_names":["gyorilab/adeft","indralab/adeft"],"tags_count":16,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gyorilab%2Fadeft","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gyorilab%2Fadeft/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gyorilab%2Fadeft/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gyorilab%2Fadeft/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gyorilab","download_url":"https://codeload.github.com/gyorilab/adeft/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251721022,"owners_count":21632756,"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":["acronym-disambiguation"],"created_at":"2024-11-17T12:19:06.705Z","updated_at":"2025-04-30T14:27:13.371Z","avatar_url":"https://github.com/gyorilab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Adeft\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.01708/status.svg)](https://doi.org/10.21105/joss.01708)\n[![DOI](https://zenodo.org/badge/156276061.svg)](https://zenodo.org/badge/latestdoi/156276061)\n[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)\n[![Tests](https://github.com/indralab/adeft/actions/workflows/tests.yml/badge.svg)](https://github.com/indralab/adeft/actions/workflows/tests.yml)\n[![Documentation](https://readthedocs.org/projects/adeft/badge/?version=latest)](https://adeft.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/adeft.svg)](https://badge.fury.io/py/adeft)\n[![Python 3](https://img.shields.io/pypi/pyversions/adeft.svg)](https://www.python.org/downloads/release/python-357/)\n\nAdeft (Acromine based Disambiguation of Entities From Text context) is a\nutility for building models to disambiguate acronyms and other abbreviations of\nbiological terms in the scientific literature. It makes use of an\nimplementation of the [Acromine](http://www.chokkan.org/research/acromine/)\nalgorithm developed by the [NaCTeM](http://www.nactem.ac.uk/index.php) at the\nUniversity of Manchester to identify possible longform expansions for\nshortforms in a text corpus.  It allows users to build disambiguation models to\ndisambiguate shortforms based on their text context. A growing number of\npretrained disambiguation models are publicly available to download through\nadeft.\n\n#### Citation\n\nIf you use Adeft in your research, please cite the paper in the Journal of\nOpen Source Software:\n\nSteppi A, Gyori BM, Bachman JA (2020). Adeft: Acromine-based Disambiguation of\nEntities from Text with applications to the biomedical literature.  *Journal of\nOpen Source Software,* 5(45), 1708, https://doi.org/10.21105/joss.01708\n\n## Installation\n\nAdeft works with Python versions 3.5 and above. It is available on PyPi and can be installed with the command\n\n    $ pip install adeft\n\nAdeft's pretrained machine learning models can then be downloaded with the command\n\n    $ python -m adeft.download\n\nIf you choose to install by cloning this repository\n\n    $ git clone https://github.com/indralab/adeft.git\n\nYou should also run\n\n    $ python setup.py build_ext --inplace\n\nat the top level of your local repository in order to build the extension module\nfor alignment based longform detection and scoring.\n\n## Using Adeft\nA dictionary of available models can be imported with `from adeft import available_models`\n\nThe dictionary maps shortforms to model names. It's possible for multiple equivalent\nshortforms to map to the same model.\n\nHere's an example of running a disambiguator for ER on a list of texts\n\n```python\nfrom adeft.disambiguate import load_disambiguator\n\ner_dd = load_disambiguator('ER')\n\n    ...\n\ner_dd.disambiguate(texts)\n```\n\nUsers may also build and train their own disambiguators. See the documention\nfor more info.\n\n\n## Documentation\n\nDocumentation is available at\n[https://adeft.readthedocs.io](http://adeft.readthedocs.io)\n\nJupyter notebooks illustrating Adeft workflows are available under `notebooks`:\n- [Introduction](notebooks/introduction.ipynb)\n- [Model building](notebooks/model_building.ipynb)\n\n\n## Testing\n\nAdeft uses `pytest` for unit testing, and uses Github Actions as a\ncontinuous integration environment. To run tests locally, make sure\nto install the test-specific requirements listed in setup.py as\n\n```bash\npip install adeft[test]\n```\n\nand download all pre-trained models as shown above.\nThen run `pytest` in the top-level `adeft` folder.\n\n## Funding\n\nDevelopment of this software was supported by the Defense Advanced Research\nProjects Agency under awards W911NF018-1-0124 and W911NF-15-1-0544, and the\nNational Cancer Institute under award U54-CA225088.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgyorilab%2Fadeft","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgyorilab%2Fadeft","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgyorilab%2Fadeft/lists"}