• Asia Pacific Journal of Corpus Research
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• v.2 no.2
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• pp.31-41
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• 2021

This paper makes a quantitative analysis of the diachronic evolution of ancient Chinese vocabulary by constructing and counting a large-scale rough annotated corpus. The texts from Si Ku Quan Shu (a collection of Chinese ancient books) are automatically segmented to obtain ancient Chinese vocabulary with time information, which is used to the statistics on word frequency, standardized type/token ratio and proportion of monosyllabic words and dissyllabic words. Through data analysis, this study has the following four findings. Firstly, the high-frequency words in ancient Chinese are stable to a certain extent. Secondly, there is no obvious dissyllabic trend in ancient Chinese vocabulary. Moreover, the Northern and Southern Dynasties (420-589 AD) and Yuan Dynasty (1271-1368 AD) are probably the two periods with the most abundant vocabulary in ancient Chinese. Finally, the unique words with high frequency in each dynasty are mainly official titles with real power. These findings break away from qualitative methods used in traditional researches on Chinese language history and instead uses quantitative methods to draw macroscopic conclusions from large-scale corpus.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.365-374
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• 2008

In this paper, we propose a model to enrich an ontology by incrementally extending the relations through variations of patterns. In order to generalize initial patterns, combinations of features are considered as candidate patterns. The candidate patterns are used to extract relations from Wikipedia, which are sorted out according to reliability based on corpus frequency. Selected patterns then are used to extract relations, while extracted relations are again used to extend the patterns of the relation. Through making variations of patterns in incremental enrichment process, the range of pattern selection is broaden and refined, which can increase coverage and accuracy of relations extracted. In the experiments with single-feature based pattern models, we observe that the features of lexical, headword, and hypernym provide reliable information, while POS and syntactic features provide general information that is useful for enrichment of relations. Based on observations on the feature types that are appropriate for each syntactic unit type, we propose a pattern model based on the composition of features as our ongoing work.