Semantic Web Dog Food

Presented at: The Sixth International Language Resources and Evaluation Conference (LREC2008)

by Kazuaki Maeda, Xiaoyi Ma, Stephanie Strassel

Parallel text is one of the most valuable resources for development of statistical machine translation systems and other NLP applications. The Linguistic Data Consortium (LDC) has supported research on statistical machine translations and other NLP applications by creating and distributing a large amount of parallel text resources for the research communities. However, manual translations are very costly, and the number of known providers that offer complete parallel text is limited. This paper presents a cost effective approach to identify parallel document pairs from sources that provide potential parallel text - namely, sources that may contain whole or partial translations of documents in the source language - using the BITS and Champollion parallel text alignment systems developed by LDC.

Keywords: Corpus (creation, annotation, etc.), Machine Translation, SpeechToSpeech Translation, Linguistics

Resource URI on the dog food server: http://data.semanticweb.org/conference/lrec/2008/papers/779

Explore this resource elsewhere:

• on Snorql
• on Sindice
• on dotAC Explorer