A Web Scale Entity Extraction System
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...
Gespeichert in:
Hauptverfasser: | , , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
DOI: | 10.48550/arxiv.2110.00423 |