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 Transformers. We empirically demonstrate the effectiveness of multilingual, 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.
Cite
CITATION STYLE
Cai, X., Ma, Q., Li, P., Liu, J., Zeng, Q., Yang, Z., & Tripathi, P. (2021). A Web Scale Entity Extraction System. In Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 (pp. 69–73). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-emnlp.7
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