A Web Scale Entity Extraction System

Xuanting Cai, Quanbin Ma, Jianyu Liu, Pan Li, Qi Zeng, Zhengkan Yang, Pushkar Tripathi


Abstract
Understanding the semantic meaning of content on the web through the lens of entities and concepts has many practical advantages. However, when building large-scale entity extraction systems, practitioners are facing unique challenges involving finding the best ways to leverage the scale and variety of data available on internet platforms. We present learnings from our efforts in building an entity extraction system for multiple document types at large scale using multi-modal Transformers. We empirically demonstrate the effectiveness of multi-lingual, multi-task and cross-document type learning. We also discuss the label collection schemes that help to minimize the amount of noise in the collected data.
Anthology ID:
2021.findings-emnlp.7
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
69–73
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.7
DOI:
10.18653/v1/2021.findings-emnlp.7
Bibkey:
Cite (ACL):
Xuanting Cai, Quanbin Ma, Jianyu Liu, Pan Li, Qi Zeng, Zhengkan Yang, and Pushkar Tripathi. 2021. A Web Scale Entity Extraction System. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 69–73, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
A Web Scale Entity Extraction System (Cai et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.7.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.7.mp4