Using Phylogenetic Networks to Model Chinese Dialect History

List, Johann‐Mattis; Nelson-Sathi, Shijulal; Martin, William; Geisler, Hans

doi:10.1163/22105832-00402008

Cited by 27 publications

(18 citation statements)

References 29 publications

(15 reference statements)

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“…Ancestral state reconstruction For our study, we tested three different established algorithms, namely (1) Maximum Parsimony (MP) reconstruction using the Sankoff algorithm (Sankoff, 1975), (2) the minimal lateral network (MLN) approach (Dagan et al, 2008) as a variant of Maximum Parsimony in which parsimony weights are selected with the help of the vocabulary size criterion (List et al, 2014b(List et al, , 2014c, and (3) Maximum Likelihood (ML) reconstruction as implemented in the software BayesTraits (Pagel and Meade, 2014). These algorithms are described in detail below.…”

Section: 22mentioning

confidence: 99%

“…The MLN approach was originally developed for the detection of lateral gene transfer events in evolutionary biology (Dagan et al, 2008). In this form, it was also applied to linguistic data (Nelson-Sathi et al, 2011), and later substantially modified (List et al, 2014b(List et al, , 2014c. While the original approach was based on very simple gain-loss-mapping techniques, the improved version uses weighted parsimony on presence-absence data of cognate set distributions.…”

Section: Minimal Lateral Network (Mln)mentioning

confidence: 99%

“…The ratio between gains and losses follows from the experience with the MLN approach, which is presented in more detail below and which essentially tests different gain-loss scenarios for their suitability to explain a given dataset. In all published studies in which the MLN approach was tested(List et al, 2014b(List et al, , 2014cList, 2015), the best gain-loss ratio reported was 2:1.Downloaded from Brill.com09/12/2020 08:15:20AM via free access…”

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Using ancestral state reconstruction methods for onomasiological reconstruction in multilingual word lists

Jäger

List

2018

Lang. Dyn. Change

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Current efforts in computational historical linguistics are predominantly concerned with phylogenetic inference. Methods for ancestral state reconstruction have only been applied sporadically. In contrast to phylogenetic algorithms, automatic reconstruction methods presuppose phylogenetic information in order to explain what has evolved when and where. Here we report a pilot study exploring how well automatic methods for ancestral state reconstruction perform in the task of onomasiological reconstruction in multilingual word lists, where algorithms are used to infer how the words evolved along a given phylogeny, and reconstruct which cognate classes were used to express a given meaning in the ancestral languages. Comparing three different methods, Maximum Parsimony, Minimal Lateral Networks, and Maximum Likelihood on three different test sets (Indo-European, Austronesian, Chinese) using binary and multi-state coding of the data as well as single and sampled phylogenies, we find that Maximum Likelihood largely outperforms the other methods. At the same time, however, the general performance was disappointingly low, ranging between 0.66 (Chinese) and 0.79 (Austronesian) for the F-Scores. A closer linguistic evaluation of the reconstructions proposed by the best method and the reconstructions given in the gold standards revealed that the majority of the cases where the algorithms failed can be attributed to problems of independent semantic shift (homoplasy), to morphological processes in lexical change, and to wrong reconstructions in the independently created test sets that we employed.

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Section: 22mentioning

confidence: 99%

Section: Minimal Lateral Network (Mln)mentioning

confidence: 99%

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Using ancestral state reconstruction methods for onomasiological reconstruction in multilingual word lists

Jäger

List

2018

Lang. Dyn. Change

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“…We must be careful, however, since it is obvious that words can easily be borrowed among languages, and closely related languages will also allow for the borrowing of full compounds, as we can see in numerous examples from the Chinese dialects (compare, for example, List et al 2014). Nevertheless, when such cases can be excluded, compound structure may serve as a proxy for the identification of shared traits between languages and thus help us to identify potential innovations that provide us evidence for subgrouping.…”

Section: Compound Structure and Subgroupingmentioning

confidence: 99%

Challenges of annotation and analysis in computer-assisted language comparison: A case study on Burmish languages

Hill

List

2017

Yearbook of the Poznan Linguistic Meeting

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The use of computational methods in comparative linguistics is growing in popularity. The increasing deployment of such methods draws into focus those areas in which they remain inadequate as well as those areas where classical approaches to language comparison are untransparent and inconsistent. In this paper we illustrate specific challenges which both computational and classical approaches encounter when studying South-East Asian languages. With the help of data from the Burmish language family we point to the challenges resulting from missing annotation standards and insufficient methods for analysis and we illustrate how to tackle these problems within a computer-assisted framework in which computational approaches are used to pre-analyse the data while linguists attend to the detailed analyses.

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“…First, since the original publication, researchers have created new linguistic trees that can provide the needed resolution. For example, the Glottolog phylogeny (Hammarström et al, 2015 ) has at least three levels of classification for the relevant varieties, though this does not have branch lengths (see also “reference” trees produced in List et al, 2014 ). Another recently published phylogeny uses lexical data to construct a phylogenetic tree for many language varieties within China (List et al, 2014 ).…”

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Commentary: Large-scale psychological differences within China explained by rice vs. wheat agriculture

Roberts

2015

Front. Psychol.

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Talhelm et al. (2014) test the hypothesis that activities which require more intensive collaboration foster more collectivist cultures. They demonstrate that a measure of collectivism correlates with the proportion of cultivated land devoted to rice paddies, which require more work to grow and maintain than other grains. The data come from individual measures of provinces in China. While the data is analyzed carefully, one aspect that is not directly controlled for is the historical relations between these provinces. Spurious correlations can occur between cultural traits that are inherited from ancestor cultures or borrowed through contact, what is commonly known as Galton's problem (Roberts and Winters, 2013). Effectively, Talhelm et al. treat the measures of each province as independent samples, while in reality both farming practices (e.g., Renfrew, 1997; Diamond and Bellwood, 2003; Lee and Hasegawa, 2011) and cultural values (e.g., Currie et al., 2010; Bulbulia et al., 2013) can be inherited or borrowed. This means that the data may be composed of non-independent points, inflating the apparent correlation between rice growing and collectivism. The correlation between farming practices and collectivism may be robust, but this cannot be known without an empirical control for the relatedness of the samples. Talhelm et al. do discuss this problem in the supplementary materials of their paper. They acknowledge that a phylogenetic analysis could be used to control for relatedness, but that at the time of publication there were no genetic or linguistic trees of descent which are detailed enough to suit this purpose. In this commentary I would like to make two points. First, since the original publication, researchers have created new linguistic trees that can provide the needed resolution. For example, the Glottolog phylogeny (Hammarström et al., 2015) has at least three levels of classification for the relevant varieties, though this does not have branch lengths (see also “reference” trees produced in List et al., 2014). Another recently published phylogeny uses lexical data to construct a phylogenetic tree for many language varieties within China (List et al., 2014). In this commentary I use these lexical data to estimate cultural contact between different provinces, and test whether these measures explain variation in rice farming pracices. However, the second point is that Talhelm et al. focus on descent (vertical transmission), while it may be relevant to control for both descent and borrowing (horizontal transmission). In this case, all that is needed is some measure of cultural contact between groups, not necessarily a unified tree of descent. I use a second source of linguistic data to calculate simple distances between languages based directly on the lexicon. These distances reflect borrowing as well as descent

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Using Phylogenetic Networks to Model Chinese Dialect History

Cited by 27 publications

References 29 publications

Using ancestral state reconstruction methods for onomasiological reconstruction in multilingual word lists

Using ancestral state reconstruction methods for onomasiological reconstruction in multilingual word lists

Challenges of annotation and analysis in computer-assisted language comparison: A case study on Burmish languages

Commentary: Large-scale psychological differences within China explained by rice vs. wheat agriculture

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