@inproceedings{loughnane-etal-2024-explicit,
    title = "Explicit Attribute Extraction in e-Commerce Search",
    author = "Loughnane, Robyn  and
      Liu, Jiaxin  and
      Chen, Zhilin  and
      Wang, Zhiqi  and
      Giroux, Joseph  and
      Du, Tianchuan  and
      Schroeder, Benjamin  and
      Sun, Weiyi",
    editor = "Malmasi, Shervin  and
      Fetahu, Besnik  and
      Ueffing, Nicola  and
      Rokhlenko, Oleg  and
      Agichtein, Eugene  and
      Guy, Ido",
    booktitle = "Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.ecnlp-1.13",
    pages = "125--135",
    abstract = "This paper presents a model architecture and training pipeline for attribute value extraction from search queries. The model uses weak labels generated from customer interactions to train a transformer-based NER model. A two-stage normalization process is then applied to deal with the problem of a large label space: first, the model output is normalized onto common generic attribute values, then it is mapped onto a larger range of actual product attribute values. This approach lets us successfully apply a transformer-based NER model to the extraction of a broad range of attribute values in a real-time production environment for e-commerce applications, contrary to previous research. In an online test, we demonstrate business value by integrating the model into a system for semantic product retrieval and ranking.",
}

This paper presents a model architecture and training pipeline for attribute value extraction from search queries. The model uses weak labels generated from customer interactions to train a transformer-based NER model. A two-stage normalization process is then applied to deal with the problem of a large label space: first, the model output is normalized onto common generic attribute values, then it is mapped onto a larger range of actual product attribute values. This approach lets us successfully apply a transformer-based NER model to the extraction of a broad range of attribute values in a real-time production environment for e-commerce applications, contrary to previous research. In an online test, we demonstrate business value by integrating the model into a system for semantic product retrieval and ranking.
Loughnane, Robyn Liu, Jiaxin Chen, Zhilin Wang, Zhiqi Giroux, Joseph Du, Tianchuan Schroeder, Benjamin Sun, Weiyi
Explicit Attribute Extraction in e-Commerce Search