Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding

Understand

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.