• For text classification and information retrieval tasks, text data has to be represented as a fixed dimension vector.
• We propose simple feature construction technique named Unsupervised Contextualized Document Representation that peforms Word Sense Disambiguation and usues contextualization power of BERT and SCDV.
• We demonstrate our method through experiments on multi-class classification ( Fully Supervised and Semi-Supervised Setthing ), Similarity Tasks & Concept Matching Tasks.

If you find SCDV useful in your research, please consider citing:

@misc{gupta2021unsupervised,
      title={Unsupervised Contextualized Document Representation}, 
      author={Ankur Gupta and Vivek Gupta},
      year={2021},
      eprint={2109.10509},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Please refer to folders classification, STS & Concept_Match which contains script and data related to experiments.

Minimum requirements:

• Python 3.7
• NumPy 1.17.2
• Scikit-learn 0.23.1
• Pandas 0.25.1
• Gensim 3.8.1
• sgmllib3k
• flair 0.9

For theory and explanation of this work, please visit our SustaiNLP paper