PCFG Parsing for Restricted Classical Chinese Texts

The Probabilistic Context-Free Grammar (PCFG) model is widely used for parsing natural languages, including Modern Chinese. But for Classical Chinese, the computer processing is just commencing. Our previous study on the part-of-speech (POS) tagging of Classical Chinese is a pioneering work in this area. Now in this paper, we move on to the PCFG parsing of Classical Chinese texts. We continue to use the same tagset and corpus as our previous study, and apply the bigram-based forward-backward algorithm to obtain the context-dependent probabilities. Then for the PCFG model, we restrict the rewriting rules to be binary/unary rules, which will simplify our programming. A small-sized rule-set was developed that could account for the grammatical phenomena occurred in the corpus. The restriction of texts lies in the limitation on the amount of proper nouns and difficult characters. In our preliminary experiments, the parser gives a promising accuracy of 82.3%.