This paper presents a comparison of classification methods for linguistic typology for the purpose of expanding an extensive, but sparse language resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath, 2013). We experimented with a variety of regression and nearest-neighbor methods for use in classification over a set of 325 languages and six syntactic rules drawn from WALS. To classify each rule, we consider the typological features of the other five rules; linguistic features extracted from a word-aligned Bible in each language; and genealogical features (genus and family) of each language. In general, we find that propagating the majority label among all languages of the same genus achieves the best accuracy in label pre- diction. Following this, a logistic regression model that combines typological and linguistic features offers the next best performance. Interestingly, this model actually outperforms the majority labels among all languages of the same family.
Typological similarity has previously been shown to correlate with genealogical similarity both in the fields of NLP @cite_4 and historical linguistics @cite_13 . Due to this, WALS has been used to study linguistic typology via computational methods. In order to determine language similarity via genealogical relatedness, it is important to know which rules are more telling of historical relatedness. To do this, @cite_0 measured the variance of linguistic rules within language families, at the genus level, to determine their stability. Rules that change less often within language families, then, are more indicative of historical relationships when they are shared between languages. To do this, the authors extracted data for language families from WALS and calculated the probability of features being shared within and across language families. Feature variation was shown to be significantly different across genera. This indicates that language families are more similar internally than across genera, which in turn supports the concept of using typological similarity to predict language similarity.
@cite_13 @cite_0 @cite_4
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The contribution of language history to the study of the early dispersals of modern humans throughout the Old World has been limited by the shallow time depth (about 8000 ± 2000 years) of current linguistic methods. Here it is shown that the application of biological cladistic methods, not to vocabulary (as has been previously tried) but to language structure (sound systems and grammar), may extend the time depths at which language data can be used. The method was tested against well-understood families of Oceanic Austronesian languages, then applied to the Papuan languages of Island Melanesia, a group of hitherto unrelatable isolates. Papuan languages show an archipelago-based phylogenetic signal that is consistent with the current geographical distribution of languages. The most plausible hypothesis to explain this result is the divergence of the Papuan languages from a common ancestral stock, as part of late Pleistocene dispersals. In this paper we propose a method to measure the stability of typological features given a data set such as (2005). We determine the frequency with which one and the same value of a feature occurs in shallow genealogical groupings (‘genus’ level). If the languages within such a group tend to show the same value, there is reason to believe that the feature is inherently more stable than if much variation is found within such groups. The proposed metric may be used to evaluate the utility of linguistic features when employing typological data for inferring genealogical relationships among languages. The recent availability of typological databases such as World Atlas of Language Structures (WALS) has spurred investigations regarding its utility for classifying the world’s languages, the stability of typological features in genetic linguistics and typological universals across the language families of the world. In this paper, we compare typological distances, derived from fifteen vector similarity measures, with family internal classifications and also lexical divergence. These results are only a first step towards the use of WALS database in the projection of NLP resources and bootstrapping NLP tools for typologically or genetically similar, yet resource-poor languages.