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cs.CL2024
Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence
Qianren Mao, Weifeng Jiang, Junnan Liu +5
The semi-supervised learning (SSL) strategy in lightweight models requires reducing annotated samples and facilitating cost-effective inference. However, the constraint on model pa…
cs.CL2018
Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding
Xu Han, Hongsu Wang, Sanqian Zhang +2
In this paper, we develop a low than character feature embedding called radical embedding, and apply it on LSTM model for sentence segmentation of pre modern Chinese texts. The dat…