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In this paper, we develop a low than character feature embedding calledradical embedding, and apply it on LSTM model for sentence segmentation of premodern Chinese texts. The datasets includes over 150 classical Chinese booksfrom 3 different dynasties and contains different literary styles. LSTM CRFmodel is a state of art method for the sequence labeling problem. Our new modeladds a component of radical embedding, which leads to improved performances.Experimental results based on the aforementioned Chinese books demonstrates abetter accuracy than earlier methods on sentence segmentation, especial in TangEpitaph texts.

Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding