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        "author": "Ayoub RMIDI",
        "author_email": "ayoub.rmidi@gmail.com",
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        "description": "# French word segmentation\n\nUsually when extracting text from open source OCRs like Tesseract, we're most likely to encounter linked words duo to OCR quality extraction. \n\nFor example :\ninstead of extracting **\"Tr\u00e8s bon service\"**, one might get sudenlly **\"Tr\u00e8s bonservice\"**. So when doing feature engineering with BOW, TFIDF or even word2vec models, the algorithm will consider that **\"bonservice\"** as a unique feature, while it is not.\n\nTo deal with this problem, I built a module dealing with semantic word segmentation without any predefined corpus.\n\n## Installation\n\nUse the package manager [pip](https://pypi.org/project/fr-word-segment/) to install fr_word_segment.\n\n```bash\npip3 install fr-word-segment\npython3 -m spacy download fr\n```\n\n## Usage\n\n```python\nfrom fr_word_segment import wordseg\n# suppose that a french spellchecker detect this token as misspelled\ntoken = \"soitmoinscompliqu\u00e9\"\n\n# apply segmentation function on the given token\nresult = wordseg.segment_token(token)\n\n# show results\nprint(\"raw token is {}\".format(token)) # \"soitmoinscompliqu\u00e9\"\nprint(\"processed token is {}\".format(result)) # \"soit moins compliqu\u00e9\"\n```\n\n## Contributing\nPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.\n\n\n## License\n[MIT](https://choosealicense.com/licenses/mit/)\n\n",
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