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 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. 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. We introduce an approach to learn discriminative visual representations while exploiting external semantic knowledge about object category relationships. Given a hierarchical taxonomy that captures semantic similarity between the objects, we learn a corresponding tree of metrics (ToM). In this tree, we have one metric for each non-leaf node of the object hierarchy, and each metric is responsible for discriminating among its immediate subcategory children. Specifically, a Mahalanobis metric learned for a given node must satisfy the appropriate (dis)similarity constraints generated only among its subtree members' training instances. To further exploit the semantics, we introduce a novel regularizer coupling the metrics that prefers a sparse disjoint set of features to be selected for each metric relative to its ancestor (supercategory) nodes' metrics. Intuitively, this reflects that visual cues most useful to distinguish the generic classes (e.g., feline vs. canine) should be different than those cues most useful to distinguish their component fine-grained classes (e.g., Persian cat vs. Siamese cat). We validate our approach with multiple image datasets using the WordNet taxonomy, show its advantages over alternative metric learning approaches, and analyze the meaning of attribute features selected by our algorithm. We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation. Our model incorporates heterogeneous relational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia abstracts. For efficient inference over taxonomy structures, we use loopy belief propagation along with a directed spanning tree algorithm for the core hypernymy factor. To train the system, we extract sub-structures of WordNet and discriminatively learn to reproduce them, using adaptive subgradient stochastic optimization. On the task of reproducing sub-hierarchies of WordNet, our approach achieves a 51 error reduction over a chance baseline, including a 15 error reduction due to the non-hypernym-factored sibling features. On a comparison setup, we find up to 29 relative error reduction over previous work on ancestor F1.