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Using Network Models to Analyze Old Chinese Rhyme Data (用網絡模型來分析古代漢語的韻母數據)

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The evidence one can draw from the rhyming behavior of Old Chinese words plays a crucial role for the reconstruction of Old Chinese, and is particularly relevant to recent proposals. Some of these proposals are no longer solely based on the intuition of scholars but also substantiated by statistical arguments that help to assess the probability by which a given set of rhyming instances can be assigned to an established rhyme group. So far, however, quantitative methods were only used to confirm given hypotheses regarding rhyme groups in Old Chinese, and no exploratory analyses that would create hypotheses regarding rhyme groups in a corpus were carried out. This paper presents a new method that models rhyme data as weighted undirected networks. By representing rhyme words as nodes in a network and the frequency of rhymes in a given corpus as links between nodes, rhyme groups can be inferred with help of standard algorithms originally designed for social network analysis. This is illustrated through the construction of a rhyme network from the Book of Odes and comparing the automatically inferred rhyme groups with rhyme groups proposed in the literature. Apart from revealing interesting general properties of rhyme networks in Chinese historical phonology, the analysis provides strong evidence for a coda *-r in Old Chinese. The results of the analysis and the rhyme network of the Book of Odes can be inspected in form of an interactive online application or directly downloaded.古代漢語的詞語所反映的韻為對上古音系的構擬,特別是對於最近的一些上古漢語構擬系統,異常重要。其中有一些構擬系統不再僅僅靠於學者的直覺,而且還用統計參數證實來評估分韻和派韻的概率。然而,迄今為止,定量方法僅用於確認關於上古韻部的假設,並且沒有進行探索性數據分析來創建初步分韻假設。本文提出了一種將韻母數據模型為加權無向網絡的新方法。此方法將韻母模型為網絡中的頂點,將某個語料庫的合韻率模型為聯頂點的邊緣,用社會網絡分析的標準算法來推斷語料庫所反映的韻母。為了更具體的說明此方法,本文用“詩經”來構建韻母網絡,而且比較自動與學者所推斷的上古韻部。除了揭示古代漢語韻網的一些有趣特點,“詩經”韻網分析了支持上古漢語韻尾* -r的新證據。“詩經”韻網和韻網分析的結果可以用交際在線應用來訪問而下載。(This article is in English.)

Affiliations: 1: CNRS-CRLAO-INALCO-EHESS mattis.list@lingpy.org ; 2: 法國國家科學研究院 mattis.list@lingpy.org

10.1163/2405478X-00902004
/content/journals/10.1163/2405478x-00902004
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2016-06-22
2018-02-19

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