超越同源:单词之间的历史关系及其对系统发育重建的影响

IF 2.1 N/A LANGUAGE & LINGUISTICS Journal of Language Evolution Pub Date : 2016-07-01 DOI:10.1093/JOLE/LZW006
Johann-Mattis List
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引用次数: 35

摘要

本文探讨了语言学中的词汇(同源)和生物学中的基因(同源)之间的基本历史关系的术语和过程。语言学和生物学的比较表明,这两个研究领域之间的类比和语言学系统发育重建中所采用的模型存在很大的不一致。词与词之间的同源关系是一种二元关系,不是存在就是不存在。然而,单词可以表现出不同程度的同源性,这超出了生物学中同源基因和同源基因之间的区别。同源性的复杂性质对用于系统发育重建的模型具有强烈的影响。我们不应该把词汇的演变建模为同源获得和同源丧失的过程,而应该超越同源关系,发展考虑同源程度的模型。这选择了使用进化模型来处理多状态特征,并允许定义特征状态之间潜在的不对称过渡趋势,而不是系统发育方法中的时间可逆二元状态模型。在简约框架下,对23种汉语方言的词典统计数据集进行了词汇变化模型在语义重构中的表现,验证了具有非对称转换倾向的多状态模型的优势。结果表明,改进后的模型在很大程度上优于常用的损益模型。这表明,改进的词汇变化模型可能会对语言学的系统发育方法产生强烈的影响。
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Beyond cognacy: historical relations between words and their implication for phylogenetic reconstruction
This article investigates the terminology and the processes underlying the fundamental historical relations between words in linguistics ( cognacy ) and genes in biology ( homology ). The comparison between linguistics and biology shows that there are major inconsistencies in the analogies drawn between the two research fields and the models applied in phylogenetic reconstruction in linguistics. Cognacy between words is treated as a binary relation which is either present or not. Words, however, can exhibit different degrees of cognacy which go beyond the distinction between orthologous and paralogous genes in biology. The complex nature of cognacy has strong implications for the models used for phylogenetic reconstruction. Instead of modeling lexical evolution as a process of cognate gain and cognate loss, we need to go beyond the cognate relation and develop models which take the degrees of cognacy into account. This opts for the use of evolutionary models which handle multistate characters and allow to define potentially asymmetrical transition tendencies among the character states instead of time-reversible binary state models in phylogenetic approaches. The benefit of multistate models with asymmetric transition tendencies is demonstrated by testing how well different models of lexical change perform in semantic reconstruction on a lexicostatistical dataset of 23 Chinese dialects in a parsimony framework. The results show that the improved models largely outperform the popular gain–loss models. This suggests that improved models of lexical change may have strong consequences for phylogenetic approaches in linguistics.
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来源期刊
Journal of Language Evolution
Journal of Language Evolution Social Sciences-Linguistics and Language
CiteScore
4.50
自引率
7.70%
发文量
8
期刊最新文献
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