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A Pragmatic Approach for Classical Chinese Word Segmentation

Word segmentation, a fundamental technology for lots of downstream applications, plays a significant role in Natural Language Processing, especially for those languages without explicit delimiters, like Chinese, Korean, Japanese and etc. Basically, word segmentation for modern Chinese is worked out to a certain extent. Nevertheless, Classical Chinese is largely neglected, mainly owing to its obsoleteness. One of the biggest problems for the researches of Classical Chinese word segmentation (CCWS) is lacking in standard large-scale shareable marked-up corpora, for the fact that the most excellent approaches, solving word segmentation, are based on machine learning or statistical methods which need quality-assured marked-up corpora. In this paper, we propose a pragmatic approach founded on the difference of t-score (dts) and Baidu Baike (the largest Chinese-language encyclopedia like Wikipedia) in order to deal with CCWS without any marked-up corpus. We extract candidate words as well as their corresponding frequency from the Twenty-Five Histories (Twenty-Four Histories and Draft History of Qing) to build a lexicon, and conduct segmentation experiments with it. The F-Score of our approach on the whole evaluation data set is 76.84%. Compared with traditional collocation-based methods, ours makes the segmentation more accurate.