{"id":21678377,"url":"https://github.com/centre-for-humanities-computing/memo-canonical-novels","last_synced_at":"2025-07-30T06:12:59.016Z","repository":{"id":262057860,"uuid":"839373382","full_name":"centre-for-humanities-computing/memo-canonical-novels","owner":"centre-for-humanities-computing","description":null,"archived":false,"fork":false,"pushed_at":"2024-11-14T19:05:29.000Z","size":15883,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-20T09:49:53.677Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# memo-canonical-novels 📚 \n\n\u003ca href=\"https://chc.au.dk\"\u003e\u003cimg src=\"https://github.com/centre-for-humanities-computing/intra/raw/main/images/onboarding/CHC_logo-turquoise-full-name.png\" width=\"25%\" align=\"right\"/\u003e\u003c/a\u003e\n[![cc](https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter)](https://cookiecutter-data-science.drivendata.org/)\n[![cc](https://img.shields.io/badge/EMNLP-NLP4DH-blue.svg?logo=data:image/png;base64,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)](https://aclanthology.org/2024.nlp4dh-1.14.pdf)\n\n\n###\n\nThis repository contains code for embeddings, plots and results for our paper: \n\n\"Canonical Status and Literary Influence: A Comparative Study of Danish Novels from the Modern Breakthrough (1870–1900)\" presented at NLP4DH at EMNLP 2024.\n\n## Useful directions 📌\n\nSome useful directions:\n- `memo_canonical_novels/` the main folder contains the source code for the project, here you will find the makefile to create embeddings\n- `notebooks/` contains the notebooks used for the analysis, `analysis.py` is the main notebook, `tfidf_comparison.py` is the notebook used to compare the embeddings with tf-idf. Other notebooks contain sanity checks.\n- `figures/` contains the figures generated by the notebooks\n- `data/` contains saved embeddings (.json) used for the analysis (and will contain generated embeddings if you generate them)\n\n## Data \u0026 paper 📝\n\nThe dataset used is available at [huggingface](https://huggingface.co/datasets/MiMe-MeMo/Corpus-v1.1)\n\nPlease cite our [paper](https://aclanthology.org/2024.nlp4dh-1.14.pdf) if you use the code or the embeddings:\n\n```\n@inproceedings{feldkamp-etal-2024-canonical,\n    title = \"Canonical Status and Literary Influence: A Comparative Study of {D}anish Novels from the Modern Breakthrough (1870{--}1900)\",\n    author = \"Feldkamp, Pascale  and\n      Lassche, Alie  and\n      Kostkan, Jan  and\n      Kardos, M{\\'a}rton  and\n      Enevoldsen, Kenneth  and\n      Baunvig, Katrine  and\n      Nielbo, Kristoffer\",\n    editor = {H{\\\"a}m{\\\"a}l{\\\"a}inen, Mika  and\n      {\\\"O}hman, Emily  and\n      Miyagawa, So  and\n      Alnajjar, Khalid  and\n      Bizzoni, Yuri},\n    booktitle = \"Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities\",\n    month = nov,\n    year = \"2024\",\n    address = \"Miami, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.nlp4dh-1.14\",\n    pages = \"140--155\"\n}\n```\n\n## Project Organization 🏗️\n\n```\n├── LICENSE            \u003c- Open-source license if one is chosen\n├── Makefile           \u003c- Makefile with convenience commands like `make data` or `make train`\n├── README.md          \u003c- The top-level README for developers using this project.\n├── data\n│   ├── interim        \u003c- Intermediate data that has been transformed.\n│   ├── processed      \u003c- The final, canonical data sets for modeling.\n│   └── raw            \u003c- The original, immutable data dump.\n│\n├── notebooks          \u003c- Jupyter notebooks.\n│\n├── pyproject.toml     \u003c- Project configuration file with package metadata for \n│                         memo_canonical_novels and configuration for tools like black\n│\n├── figures            \u003c- Generated graphics and figures to be used in reporting\n│\n├── requirements.txt   \u003c- The requirements file for reproducing the analysis environment, e.g.\n│                         generated with `pip freeze \u003e requirements.txt`\n│\n├── setup.cfg          \u003c- Configuration file for flake8\n│\n└── src                \u003c- Source code for use in this project, making embeddings.\n    │\n    ├── __init__.py             \u003c- Makes memo_canonical_novels a Python module\n    │\n    ├── config.py               \u003c- Store useful variables and configuration\n    │\n    ├── dataset.py              \u003c- Scripts to download or generate data\n    │\n    ├── features.py             \u003c- Code to create features for modeling\n    │\n    ├── modeling                \n    │   ├── __init__.py \n    │   ├── predict.py          \u003c- Code to run model inference with trained models          \n    │   └── train.py            \u003c- Code to train models\n    └── pooling.py              \u003c- Code to create average embeddings from raw embeddings\n    │\n    └── plots.py                \u003c- Code to create visualizations\n```\n\n--------\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcentre-for-humanities-computing%2Fmemo-canonical-novels","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcentre-for-humanities-computing%2Fmemo-canonical-novels","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcentre-for-humanities-computing%2Fmemo-canonical-novels/lists"}