It has long been recognized that suffixing is more common than prefixing in the languages of the world. More detailed statistics on this tendency are needed to sharpen proposed explanations for this tendency. The classic approach to gathering data on the prefix/suffix preference is for a human to read grammatical descriptions (948 languages), which is time-consuming and involves discretization judgments. In this paper we explore two machine-driven approaches for prefix and suffix statistics which are crude approximations, but have advantages in terms of time and replicability. The first simply searches a large collection of grammatical descriptions for occurrences of the terms ‘prefix’ and ‘suffix’ (4 287 languages). The second counts substrings from raw text data in a way indirectly reflecting prefixation and suffixation (1 030 languages, using New Testament translations). The three approaches largely agree in their measurements but there are important theoretical and practical differences. In all measurements, there is an overall preference for suffixation, albeit only slightly, at ratios ranging between 0.51 and 0.68.

Language catalogues and typological databases are two important types of resources containing different types of knowledge about the world’s natural languages. The former provide metadata such as number of speakers, location (in prose descriptions and/or GPS coordinates), language code, literacy, etc., while the latter contain information about a set of structural and functional attributes of languages. Given that both types of resources are developed and later maintained manually, there are practical limits as to the number of languages and the number of features that can be surveyed. We introduce the concept of a language profile, which is intended to be a structured representation of various types of knowledge about a natural language extracted semi-automatically from descriptive documents and stored at a central location. It has three major parts: (1) an introductory; (2) an attributive; and (3) a reference part, each containing different types of knowledge about a given natural language. As a case study, we develop and present a language profile of an example language. At this stage, a language profile is an independent entity, but in the future it is envisioned to become part of a network of language profiles connected to each other via various types of relations. Such a representation is expected to be suitable both for humans and machines to read and process for further deeper linguistic analyses and/or comparisons.

There exist as many as 7000 natural languages in the world, and a huge number of documents describing those languages have been produced over the years. Most of those documents are in paper format. Any attempts to use modern computational techniques and tools to process those documents will require them to be digitized first. In this paper, we report a multilingual digitized version of thousands of such documents searchable through some well-established corpus infrastructures. The corpus is annotated with various meta, word, and text level attributes to make searching and analysis easier and more useful.

Language resources can be divided into structural resources treating phonology, morphosyntax, semantics etc. and resources treating the social, demographic, ethnic, political context. A third type are meta-resources, like bibliographies, which provide access to the resources of the first two kinds. This poster will present the Glottolog/Langdoc project, a comprehensive bibliography providing web access to 180k bibliographical records to (mainly) low visibility resources from low-density languages. The resources are annotated for macro-area, content language, and document type and are available in XHTML and RDF.

This paper describes past, ongoing and planned work on the collection and transcription of spoken language samples for all the South African official languages and as part of this the training of researchers in corpus linguistic research skills. More specifically the work has involved (and still involves) establishing an international corpus linguistic network linked to a network hub at a UNISA website and the development of research tools, a corpus research guide and workbook for multimodal communication and spoken language corpus research. As an example of the work we are doing and hope to do more of in the future, we present a small pilot study of the influence of English and Afrikaans on the 100 most frequent words in spoken Xhosa as this is evidenced in the corpus of spoken interaction we have gathered so far. Other planned work, besides work on spoken language phenomena, involves comparison of spoken and written language and work on communicative body movements (gestures) and their relation to speech.

Linguists have long been producing grammatical decriptions of yet undescribed languages. This is a time-consuming process, which has already adapted to improved technology for recording and storage. We present here a novel application of NLP techniques to bootstrap analysis of collected data and speed-up manual selection work. To be more precise, we argue that unsupervised induction of morphology and part-of-speech analysis from raw text data is mature enough to produce useful results. Experiments with Latent Semantic Analysis were less fruitful. We exemplify this on Mpiemo, a so-far essentially undescribed Bantu language of the Central African Republic, for which raw text data was available.