Analyzing who, what, and where in a mediaeval Chinese corpus: A case study on the Chinese Buddhist Canon

Information extraction from historical text is challenging because of the lack of data to train natural language processing tools. This chapter evaluates the utility of in-domain training data for data-driven profiling of characters, verbs, and toponyms and reports a case study on a corpus of Chinese Buddhist text. As is typical for such a corpus, the Chinese Buddhist Canon has few annotated linguistic resources other than lexica of names, places, and domain-specific terms. We apply a lexicon-based approach for named entity recognition and then report an analysis of the “who,” “what,” and “where” of the Canon: who the characters were, what they did, and where they were. Experimental results also show that even a small amount of word segmentation, part-of-speech, and dependency annotation can improve accuracy in named entity recognition and in extraction of character-verb associations.