2024
NAACL
NAACL 2024
Improving Multi-Label Classification of Similar Languages by Semantics-Aware Word Embeddings
Abstract
AbstractThe VLP team participated in the DSL-ML shared task of the VarDial 2024 workshop which aims to distinguish texts in similar languages. This paper presents our approach to solving the problem and discusses our experimental and official results. We propose to integrate semantics-aware word embeddings which are learned from ConceptNet into a bidirectional long short-term memory network. This approach achieves good performance – our sys- tem is ranked in the top two or three of the best performing teams for the task.
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
Authors
Topics
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Text Representation
Machine Learning > Learning Types > Transfer Learning
Machine Learning > Learning Types > Multi-Label Learning