Computerization of African languages-French dictionaries

This paper relates work done during the DiLAF project. It consists in converting 5 bilingual African language-French dictionaries originally in Word format into XML following the LMF model. The languages processed are Bambara, Hausa, Kanuri, Tamajaq and Songhai-zarma, still considered as under-resourced languages concerning Natural Language Processing tools. Once converted, the dictionaries are available online on the Jibiki platform for lookup and modification. The DiLAF project is first presented. A description of each dictionary follows. Then, the conversion methodology from .doc format to XML files is presented. A specific point on the usage of Unicode follows. Then, each step of the conversion into XML and LMF is detailed. The last part presents the Jibiki lexical resources management platform used for the project.Comment: 8 page

Lost in translation: the problems of using mainstream MT evaluation metrics for sign language translation

In this paper we consider the problems of applying corpus-based techniques to minority languages that are neither politically recognised nor have a formally accepted writing system, namely sign languages. We discuss the adoption of an annotated form of sign language data as a suitable corpus for the development of a data-driven machine translation (MT) system, and deal with issues that arise from its use. Useful software tools that facilitate easy annotation of video data are also discussed. Furthermore, we address the problems of using traditional MT evaluation metrics for sign language translation. Based on the candidate translations produced from our example-based machine translation system, we discuss why standard metrics fall short of providing an accurate evaluation and suggest more suitable evaluation methods

Towards the automatic processing of Yongning Na (Sino-Tibetan): developing a 'light' acoustic model of the target language and testing 'heavyweight' models from five national languages

International audienceAutomatic speech processing technologies hold great potential to facilitate the urgent task of documenting the world's languages. The present research aims to explore the application of speech recognition tools to a little-documented language, with a view to facilitating processes of annotation, transcription and linguistic analysis. The target language is Yongning Na (a.k.a. Mosuo), an unwritten Sino-Tibetan language with less than 50,000 speakers. An acoustic model of Na was built using CMU Sphinx. In addition to this 'light' model, trained on a small data set (only 4 hours of speech from 1 speaker), 'heavyweight' models from five national languages (English, French, Chinese, Vietnamese and Khmer) were also applied to the same data. Preliminary results are reported, and perspectives for the long road ahead are outlined

Natural Language Processing for Under-resourced Languages: Developing a Welsh Natural Language Toolkit

Language technology is becoming increasingly important across a variety of application domains which have become common place in large, well-resourced languages. However, there is a danger that small, under-resourced languages are being increasingly pushed to the technological margins. Under-resourced languages face significant challenges in delivering the underlying language resources necessary to support such applications. This paper describes the development of a natural language processing toolkit for an under-resourced language, Cymraeg (Welsh). Rather than creating the Welsh Natural Language Toolkit (WNLT) from scratch, the approach involved adapting and enhancing the language processing functionality provided for other languages within an existing framework and making use of external language resources where available. This paper begins by introducing the GATE NLP framework, which was used as the development platform for the WNLT. It then describes each of the core modules of the WNLT in turn, detailing the extensions and adaptations required for Welsh language processing. An evaluation of the WNLT is then reported. Following this, two demonstration applications are presented. The first is a simple text mining application that analyses wedding announcements. The second describes the development of a Twitter NLP application, which extends the core WNLT pipeline. As a relatively small-scale project, the WNLT makes use of existing external language resources where possible, rather than creating new resources. This approach of adaptation and reuse can provide a practical and achievable route to developing language resources for under-resourced languages