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Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
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Using Titles vs. Full-text as Source for Automated Semantic Document Annotation

Lukas Galke
Florian Mai
Alan Schelten
Dennis Brunsch
A. Scherp
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Papers citing "Using Titles vs. Full-text as Source for Automated Semantic Document Annotation"

3 / 3 papers shown
Title
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets
Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets
Fabian Karl
A. Scherp
VLM
74
20
0
30 Nov 2022
Towards an Open Platform for Legal Information
Towards an Open Platform for Legal Information
Malte Ostendorff
Till Blume
Saskia Ostendorff
AILaw
33
38
0
27 May 2020
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels
Lukas Galke
Florian Mai
Iacopo Vagliano
A. Scherp
39
26
0
22 Jul 2019