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We aim at segmenting words in the Complete Tang Poems (CTP). Although it ispossible to do some research about CTP without doing full-scale wordsegmentation, we must move forward to word-level analysis of CTP for conductingadvanced research topics. In November 2018 when we submitted the manuscript forDH 2019 (ADHO), we collected only 2433 poems that were segmented by trainedexperts, and used the segmented poems to evaluate the segmenter that considereddomain knowledge of Chinese poetry. We trained pointwise mutual information(PMI) between Chinese characters based on the CTP poems (excluding the 2433poems, 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 inthe test poems. We could segment a poem completely correct only 17.8% of thetime, however. When we presented our work at DH 2019, we have annotated morethan 20000 poems. With a much larger amount of data, we were able to applybiLSTM models for this word segmentation task, and we segmented a poemcompletely correct above 20% of the time. In contrast, human annotatorscompletely agreed on their annotations about 40% of the time.

Onto Word Segmentation of the Complete Tang Poems