  We consider three major text sources about the Tang Dynasty of China in our
experiments that aim to segment text written in classical Chinese. These
corpora include a collection of Tang Tomb Biographies, the New Tang Book, and
the Old Tang Book. We show that it is possible to achieve satisfactory
segmentation results with the deep learning approach. More interestingly, we
found that some of the relative superiority that we observed among different
designs of experiments may be explainable. The relative relevance among the
training corpora provides hints/explanation for the observed differences in
segmentation results that were achieved when we employed different combinations
of corpora to train the classifiers.
