Classical Chinese Sentence Segmentation for Tomb Biographies of Tang Dynasty
This work addresses a domain-specific challenge in digital humanities by automating sentence segmentation for historical Chinese texts, which is incremental as it applies existing methods to new data.
The authors tackled the problem of segmenting classical Chinese tomb biographies from the Tang dynasty into sentences using machine learning, achieving F1 scores over 80% with conditional random fields and further improvements with deep neural networks.
Tomb biographies of the Tang dynasty provide invaluable information about Chinese history. The original biographies are classical Chinese texts which contain neither word boundaries nor sentence boundaries. Relying on three published books of tomb biographies of the Tang dynasty, we investigated the effectiveness of employing machine-learning methods for algorithmically identifying the pauses and terminals of sentences in the biographies. We consider the segmentation task as a classification problem. Chinese characters that are and are not followed by a punctuation mark are classified into two categories. We applied a machine-learning-based mechanism, the conditional random fields (CRF), to classify the characters (and words) in the texts, and we studied the contributions of selected types of lexical information to the resulting quality of the segmentation recommendations. This proposal presented at the DH 2018 conference discussed some of the basic experiments and their evaluations. By considering the contextual information and employing the heuristics provided by experts of Chinese literature, we achieved F1 measures that were better than 80%. More complex experiments that employ deep neural networks helped us further improve the results in recent work.