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Homemade BookCorpus

You can reproduce BookCorpus by yourself.

BookCorpus is a popular large-scale text corpus, espetially for unsupervised learning of sentence encoders/decoders. However, BookCorpus is no longer distributed...

This repository includes a crawler collecting data from smashwords.com, which is the original source of BookCorpus. Collected sentences may partially differ but the number of them will be larger or almost the same.

How to use

Prepare URLs of available books. However, this repository already has a list as url_list.jsonl which was a snapshot I (@soskek) collected on Jan 19-20, 2019. You can use it if you'd like.

python -u download_list.py > url_list.jsonl &

Download their files. Downloading is performed for txt files if possible. Otherwise, this tries to extract text from epub. The additional argument --trash-bad-count filters out epub files whose word count is largely different from its official stat (because it may imply some failure).

python download_files.py --list url_list.jsonl --out out_txts --trash-bad-count

Make concatenated text with sentence-per-line format.

python make_sentlines.py out_txts > all.txt

If you want to tokenize them into segmented words by nltk, run the below. You can use another choices for this by yourself.

python make_sentlines.py out_txts | python tokenize_sentlines.py > all.tokenized.txt

Requirement

  • python3 is recommended
  • beautifulsoup4
  • progressbar2
  • nltk
    • And, download tokenizers by python -c "import nltk;nltk.download('punkt')"
  • html2text

To install the requirements, run the following with pip on python3:

pip install -r requirements.txt
python -c "import nltk;nltk.download('punkt')"

Note

  • It is expected some error messages like Failed: epub and txt are sometimes displayed. This code does not adapt to some of books in the smashwords.com (e.g. You set the price! books).

Acknowledgement

epub2txt.py is derived and modified from https://github.com/kevinxiong/epub2txt/blob/master/epub2txt.py

Citation

Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler. "Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books." arXiv preprint arXiv:1506.06724, ICCV 2015.

@InProceedings{Zhu_2015_ICCV,
    title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
    author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {December},
    year = {2015}
}

@inproceedings{moviebook,
    title = {Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books},
    author = {Yukun Zhu and Ryan Kiros and Richard Zemel and Ruslan Salakhutdinov and Raquel Urtasun and Antonio Torralba and Sanja Fidler},
    booktitle = {arXiv preprint arXiv:1506.06724},
    year = {2015}
}

Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. "Skip-Thought Vectors." arXiv preprint arXiv:1506.06726, NIPS 2015.

@article{kiros2015skip,
    title={Skip-Thought Vectors},
    author={Kiros, Ryan and Zhu, Yukun and Salakhutdinov, Ruslan and Zemel, Richard S and Torralba, Antonio and Urtasun, Raquel and Fidler, Sanja},
    journal={arXiv preprint arXiv:1506.06726},
    year={2015}
}