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Tomb biographies of the Tang dynasty provide invaluable information aboutChinese history. The original biographies are classical Chinese texts whichcontain neither word boundaries nor sentence boundaries. Relying on threepublished books of tomb biographies of the Tang dynasty, we investigated theeffectiveness of employing machine-learning methods for algorithmicallyidentifying the pauses and terminals of sentences in the biographies.  We consider the segmentation task as a classification problem. Chinesecharacters that are and are not followed by a punctuation mark are classifiedinto two categories. We applied a machine-learning-based mechanism, theconditional random fields (CRF), to classify the characters (and words) in thetexts, and we studied the contributions of selected types of lexicalinformation to the resulting quality of the segmentation recommendations.  This proposal presented at the DH 2018 conference discussed some of the basicexperiments and their evaluations. By considering the contextual informationand employing the heuristics provided by experts of Chinese literature, weachieved F1 measures that were better than 80%. More complex experiments thatemploy deep neural networks helped us further improve the results in recentwork.

Classical Chinese Sentence Segmentation for Tomb Biographies of Tang Dynasty