10.5281/zenodo.4306169
https://zenodo.org/records/4306169
oai:zenodo.org:4306169
Vaik, Kristiina
Kristiina
Vaik
Texta OU
Asula, Marit
Marit
Asula
Texta OU
Sirel, Raul
Raul
Sirel
Texta OU
Hybrid Tagger – An Industry-driven Solution for Extreme Multi-label Text Classification
Zenodo
2020
text classification
extreme multi-label classification
data processing workflows
2020-12-04
10.5281/zenodo.4306168
https://zenodo.org/communities/embeddia
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
This paper presents an industry-driven solution for extreme multi-label classification with a massive label collection. The proposed approach incorporates a large number of binary classification models with label pre-filtering and employs methods and technologies shown to be applicable in industrial scenarios where high-end computational hardware is limited. The system is evaluated on an Estonian newspaper article dataset which contains almost 2000 unique labels and has shown to perform over 80 times faster than applying all the binary models of the entire label set without negative impact on prediction scores.
European Commission
10.13039/501100000780
825153
Cross-Lingual Embeddings for Less-Represented Languages in European News Media