Alation studies reveal a strong synergy between POS features and CRF decoding, with the combined approach yielding the best single-model performance, and ensembling models trained with different objectives improves both overall F1 score and discontinuous MWE scores, demonstrating the importance of training diversity for capturing non-adjacent syntactic patterns.
The results show that hybrid ensemble methods can improve token-level accuracy in low-resource POS tagging, while also revealing a trade-off between frequent-tag accuracy and rare-tag robustness.
Abdelouahed Moussaoui, Nor-Eddine Azalmad, Said Bahassine et al.· Information· 0 citations
The results of the experiments reveal that the proposed DistilBERT-based model outperforms the baseline Conditional Random Field and Bidirectional Long Short-Term Memory models with an accuracy 79.05%, precision 80.63%, recall 79.05% and F1-score 79.24%.
Maureen Otieno, L. Wanzare, Calvins Otieno· International Journal of Com...· 0 citations
A PMI-based translation metric is proposed, which is less dependent on the target language and correlates strongly with chrF, and finds that CLA with English predicts translation quality comparably to or better than source-target CLA.
Adnan Al Ali, Kathy Hämmerl, Jindrich Libovický et al.· 0 citations
A robust WSD model that integrates a bidirectional long shortterm memory network (BiLSTM) with an attention mechanism, specifically designed for Chinese patent texts is proposed, providing reliable support for knowledge mining and intelligent text processing in technical domains.
L.-X. Gao, T. Dong, M.-H. Yang· Advanced Electromagnetics· 0 citations
Pretrained language models (PLMs) have established state-of-the-art performance across diverse natural language understanding (NLU) tasks. This study reveals that seman-tic-rich explanations of lexical units can effectively guide PLM learning processes. We propose a novel language understanding enhancement method with...
Tianyi Chen, Yashen Wang, Huan Chang et al.· IEEE/CAA Journal of Automati...· 0 citations
A novel hybrid deep learning architecture that synergizes contextual language modeling with explicit domain knowledge and integrates a domain specific Roman Urdu hate lexicon as an auxiliary regression supervision signal within a multi task learning framework to guide the model’s focus.
Jaweria Jalil Awan, Muhammad Hamid, T. Alshalali· PLoS ONE· 0 citations
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