@inproceedings{chai-etal-2021-automatic,
    title = "Automatic Construction of Enterprise Knowledge Base",
    author = "Chai, Junyi  and
      He, Yujie  and
      Hashemi, Homa  and
      Li, Bing  and
      Parveen, Daraksha  and
      Kondapally, Ranganath  and
      Xu, Wenjin",
    editor = "Adel, Heike  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-demo.2",
    doi = "10.18653/v1/2021.emnlp-demo.2",
    pages = "11--19",
    abstract = "In this paper, we present an automatic knowledge base construction system from large scale enterprise documents with minimal efforts of human intervention. In the design and deployment of such a knowledge mining system for enterprise, we faced several challenges including data distributional shift, performance evaluation, compliance requirements and other practical issues. We leveraged state-of-the-art deep learning models to extract information (named entities and definitions) at per document level, then further applied classical machine learning techniques to process global statistical information to improve the knowledge base. Experimental results are reported on actual enterprise documents. This system is currently serving as part of a Microsoft 365 service.",
}