汉语普通话后缀生产力的测量与比较

Eiji Nishimoto
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引用次数: 15

摘要

本研究试图测量和比较普通话五种词缀的形态生产力:动词缀“花”、复数词缀“men”和名义词缀“r”、“字”和“头”。据预测,这些后缀的效率不同:-hua和-men似乎效率很高,能够系统地用各种基本词组成一个词,而-zi和-tou(也许还有-r)的效率可能有限。Baayen[1989,1992]提出使用语料库数据来衡量构词法的生产力。基于大量文本语料库中的单词标记频率,他的基于标记的生产率度量将生产率表示为语料库中遇到词缀的新单词形式的概率。我们首先使用基于符号的度量来检查普通话后缀的生产力。因此,本研究提出了一种基于类型的生产力衡量方法,该方法采用删除估计方法[Jelinek & Mercer, 1985]来定义语料库中的未见词,并通过未见词类型与所有词类型的比例来表示生产力。提出的基于类型的测量方法产生了生产率排名“-men, -hua, -r, -zi, -tou”,其中-men是生产率最高的,-tou是生产率最低的。语料库数据变异性对生产率度量的影响也进行了检验。尽管语料库数据存在差异,但所提出的度量方法可以获得一致的生产率排名。
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Measuring and Comparing the Productivity of Mandarin Chinese Suffixes
The present study attempts to measure and compare the morphological productivity of five Mandarin Chinese suffixes: the verbal suffix -hua, the plural suffix -men, and the nominal suffixes -r, -zi, and -tou. These suffixes are predicted to differ in their degree of productivity : -hua and -men appear to be productive, being able to systematically form a word with a variety of base words, whereas -zi and -tou (and perhaps also -r) may be limited in productivity. Baayen [1989, 1992] proposes the use of corpus data in measuring productivity in word formation. Based on word-token frequencies in a large corpus of texts, his token-based measure of productivity expresses productivity as the probability that a new word form of an affix will be encountered in a corpus. We first use the token-based measure to examine the productivity of the Mandarin suffixes. The present study, then, proposes a type-based measure of productivity that employs the deleted estimation method [Jelinek & Mercer, 1985] in defining unseen words of a corpus and expresses productivity by the ratio of unseen word types to all word types. The proposed type-based measure yields the productivity ranking “-men, -hua, -r, -zi, -tou,” where -men is the most productive and -tou is the least productive. The effects of corpus-data variability on a productivity measure are also examined. The proposed measure is found to obtain a consistent productivity ranking despite variability in corpus data.
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