  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 datasets includes over 150 classical Chinese books
from 3 different dynasties and contains different literary styles. LSTM CRF
model is a state of art method for the sequence labeling problem. Our new model
adds a component of radical embedding, which leads to improved performances.
Experimental results based on the aforementioned Chinese books demonstrates a
better accuracy than earlier methods on sentence segmentation, especial in Tang
Epitaph texts.
