Networks of lexical borrowing and lateral gene transfer in language and genome evolution

Abstract

Like biological species, languages change over time. As noted by Darwin, there are many parallels between language evolution and biological evolution. Insights into these parallels have also undergone change in the past 150 years. Just like genes, words change over time, and language evolution can be likened to genome evolution accordingly, but what kind of evolution? There are fundamental differences between eukaryotic and prokaryotic evolution. In the former, natural variation entails the gradual accumulation of minor mutations in alleles. In the latter, lateral gene transfer is an integral mechanism of natural variation. The study of language evolution using biological methods has attracted much interest of late, most approaches focusing on language tree construction. These approaches may underestimate the important role that borrowing plays in language evolution. Network approaches that were originally designed to study lateral gene transfer may provide more realistic insights into the complexities of language evolution.

Chances and Challenges: 從演化網絡的角度來研究漢語方言史——機遇與挑戰

“…In List et al (2014a), this was done by testing to which degree known borrowings in an Indo-European dataset were readily identified by the method. In this study, the mln approach identified 72% of all known borrowings correctly.…”

“…In a pilot study by Nelson-Sathi et al (2011), the mln approach was used to assess borrowing frequencies during Indo-European language history. In List et al (2014a), an improved version of this approach was presented and successfully applied to a small set of 40 Indo-European languages, where it identified 72% of all known borrowings in the data. In List et al (2014b), the method was applied to Chinese dialect data, where it revealed a much higher amount of characters suggestive of borrowing than was inferred for Indo-European (48-55% in Chinese vs. 31% in Indo-European).…”

Using hybridization networks to retrace the evolution of Indo-European languages

“…It is important to note that the MLN approach is an automatic approach based on tree topology and the 42 suggestive cases of borrowing recovered by MLN, which include 33 English loanwords identified by Donohue et al [22], cannot be considered as crystal-clear borrowings. They may comprise some false positives, which can be due to parallel semantic development [21], for example.…”

“…While the use of this list may lead to a certain decrease in the number of loanwords [20], it remains helpful for detecting the most important word borrowing trends [21]. For example, the traditional 200-meaning English Swadesh list includes 33 confirmed loanwords (16.5 %) [22] and 10 additional “irregular phylogenetic patterns” which might be suggestive of unrecognized borrowings [21]. Moreover, in a recent revision of the Albanian Swadesh list 31.8 % of its entries were identified as probable borrowings [23].…”

“…One of the main advantages of our method over the MLN [10, 11, 21] and perfect networks [26] approaches is that it allows for determining the direction of reticulation events (e.g., word borrowing events) in addition to the quantification of influence of each of the donor languages on the corresponding recipient languages. For a more complete description of the benefits and shortcomings of the MLN approach, the reader is referred to [40–42].…”