  We aim at segmenting words in the Complete Tang Poems (CTP). Although it is
possible to do some research about CTP without doing full-scale word
segmentation, we must move forward to word-level analysis of CTP for conducting
advanced research topics. In November 2018 when we submitted the manuscript for
DH 2019 (ADHO), we collected only 2433 poems that were segmented by trained
experts, and used the segmented poems to evaluate the segmenter that considered
domain knowledge of Chinese poetry. We trained pointwise mutual information
(PMI) between Chinese characters based on the CTP poems (excluding the 2433
poems, which were used exclusively only for testing) and the domain knowledge.
The segmenter relied on the PMI information to the recover 85.7% of words in
the test poems. We could segment a poem completely correct only 17.8% of the
time, however. When we presented our work at DH 2019, we have annotated more
than 20000 poems. With a much larger amount of data, we were able to apply
biLSTM models for this word segmentation task, and we segmented a poem
completely correct above 20% of the time. In contrast, human annotators
completely agreed on their annotations about 40% of the time.

Computation and Language;Artificial Intelligence;Machine Learning