@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automatic Construction of Enterprise Knowledge Base
%A Chai, Junyi
%A He, Yujie
%A Hashemi, Homa
%A Li, Bing
%A Parveen, Daraksha
%A Kondapally, Ranganath
%A Xu, Wenjin
%Y Adel, Heike
%Y Shi, Shuming
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F chai-etal-2021-automatic
%X 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.
%R 10.18653/v1/2021.emnlp-demo.2
%U https://aclanthology.org/2021.emnlp-demo.2
%U https://doi.org/10.18653/v1/2021.emnlp-demo.2
%P 11-19
Markdown (Informal)
[Automatic Construction of Enterprise Knowledge Base](https://aclanthology.org/2021.emnlp-demo.2) (Chai et al., EMNLP 2021)
ACL
- Junyi Chai, Yujie He, Homa Hashemi, Bing Li, Daraksha Parveen, Ranganath Kondapally, and Wenjin Xu. 2021. Automatic Construction of Enterprise Knowledge Base. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 11–19, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.