On a new 144,000-word annotated dataset for Wolayta and Gofa, BERT-base-uncased embeddings with an LSTM classifier reach 0.72 F1, the best of seven compared approaches.
”Semantic-driven topic modeling using transformer-based embeddings and clustering algorithms.” Procedia Computer Sci- ence 244 (2024): 121-132
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Bilingual Word Level Language Identification for Omotic Languages
On a new 144,000-word annotated dataset for Wolayta and Gofa, BERT-base-uncased embeddings with an LSTM classifier reach 0.72 F1, the best of seven compared approaches.