@inproceedings{shah-etal-2018-neural,
    title = "Neural Network based Extreme Classification and Similarity Models for Product Matching",
    author = "Shah, Kashif  and
      Kopru, Selcuk  and
      Ruvini, Jean-David",
    editor = "Bangalore, Srinivas  and
      Chu-Carroll, Jennifer  and
      Li, Yunyao",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-3002",
    doi = "10.18653/v1/N18-3002",
    pages = "8--15",
    abstract = "Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5{\%} in term of accuracy and are capable of extremely efficient training and inference.",
}