Automatic Construction of Enterprise Knowledge Base

Junyi Chai, Yujie He, Homa Hashemi, Bing Li, Daraksha Parveen, Ranganath Kondapally, Wenjin Xu


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.
Anthology ID:
2021.emnlp-demo.2
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–19
Language:
URL:
https://aclanthology.org/2021.emnlp-demo.2
DOI:
10.18653/v1/2021.emnlp-demo.2
Bibkey:
Cite (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.
Cite (Informal):
Automatic Construction of Enterprise Knowledge Base (Chai et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-demo.2.pdf