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.
BibTeX
@inproceedings{cai-etal-2021-web-scale,
title = "A Web Scale Entity Extraction System",
author = "Cai, Xuanting and
Ma, Quanbin and
Liu, Jianyu and
Li, Pan and
Zeng, Qi and
Yang, Zhengkan and
Tripathi, Pushkar",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.7/",
doi = "10.18653/v1/2021.findings-emnlp.7",
pages = "69--73"
}