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.
@cite_20 attempted a similar task, using interlinear glossed text (IGT) to predict typological features such as major consituent order and case system. @cite_3 also predict typological features from IGT by first learning a context free grammar for a language and then examining its structure. Both studies use data from WALS to evaluate their performance. We address a similar task, but we utilize projected dependency parses from English text as a source of knowledge for each foreign language, as many do not have extensive resources of their own. This is similar to IGT, but does not contain morphological information to the same extent. This method of projecting dependencies has been used to bootstrap linguistic resources in the past @cite_25 , @cite_11 .
@cite_3 @cite_25 @cite_20 @cite_11
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In this paper we explore the potential for identifying computationally relevant typological features from a multilingual corpus of language data built from readily available language data collected off the Web. Our work builds on previous structural projection work, where we extend the work of projection to building individual CFGs for approximately 100 languages. We then use the CFGs to discover the values of typological parameters such as word order, the presence or absence of definite and indefinite determiners, etc. Our methods have the potential of being extended to many more languages and parameters, and can have significant effects on current research focused on tool and resource development for low-density languages and grammar induction from raw corpora. This paper explores the potential for annotating and enriching data for low-density languages via the alignment and projection of syntactic structure from parsed data for resource-rich languages such as English. We seek to develop enriched resources for a large number of the world’s languages, most of which have no significant digital presence. We do this by tapping the body of Web-based linguistic data, most of which exists in small, analyzed chunks embedded in scholarly papers, journal articles, Web pages, and other online documents. By harvesting and enriching these data, we can provide the means for knowledge discovery across the resulting corpus that can lead to building computational resources such as grammars and transfer rules, which, in turn, can be used as bootstraps for building additional tools and resources for the languages represented. 1 We propose to bring together two kinds of linguistic resources—interlinear glossed text (IGT) and a language-independent precision grammar resource—to automatically create precision grammars in the context of language documentation. This paper takes the first steps in that direction by extracting major-constituent word order and case system properties from IGT for a diverse sample of languages. Broad coverage, high quality parsers are available for only a handful of languages. A prerequisite for developing broad coverage parsers for more languages is the annotation of text with the desired linguistic representations (also known as “treebanking”). However, syntactic annotation is a labor intensive and time-consuming process, and it is difficult to find linguistically annotated text in sufficient quantities. In this article, we explore using parallel text to help solving the problem of creating syntactic annotation in more languages. The central idea is to annotate the English side of a parallel corpus, project the analysis to the second language, and then train a stochastic analyzer on the resulting noisy annotations. We discuss our background assumptions, describe an initial study on the “projectability” of syntactic relations, and then present two experiments in which stochastic parsers are developed with minimal human intervention via projection from English.