ODIN: A Model for Adapting and Enriching Legacy Infrastructure
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Language ID in the context of harvesting language data off the web
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
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Until very recently, most NLP tasks (e.g., parsing, tagging, etc.) have been confined to a very limited number of languages, the so-called majority languages. Now, as the field moves into the era of developing tools for Resource Poor Languages (RPLs)--a vast majority of the world's 7,000 languages are resource poor--the discipline is confronted not only with the algorithmic challenges of limited data, but also the sheer difficulty of locating data in the first place. In this demo, we present a resource which taps the large body of linguistically annotated data on the Web, data which can be repurposed for NLP tasks. Because the field of linguistics has as its mandate the study of human language--in fact, the study of all human languages--and has whole-heartedly embraced the Web as a means for disseminating linguistic knowledge, the consequence is that a large quantity of analyzed language data can be found on the Web. In many cases, the data is richly annotated and exists for many languages for which there would otherwise be very limited annotated data. The resource, the Online Database of INterlinear text (ODIN), makes this data available and provides additional annotation and structure, making the resource useful to the Computational Linguistic audience.