Two Multilingual Corpora Extracted from the Tenders Electronic Daily for Machine Learning and Machine Translation Applications.

Abstract

The European "Tenders Electronic Daily" (TED) is a large source of semi-structured and multilingual data that is very valuable to the Natural Language Processing community. This data sets can effectively be used to address complex machine translation, multilingual terminology extraction, text-mining, or to benchmark information retrieval systems. Despite of the services offered by the user-friendliness of the web site that is made available to the public to access the publishing of the EU call for tenders, collecting and managing such kind of data is a great burden and consumes a lot of time and computing resources. This could explain why such a resource is not very (if any) exploited today by computer scientists or engineers in NLP. The aim of this paper is to describe two documented and easy-to-use multilingual corpora (one of them is a parallel corpus), extracted from the TED web source that we will release for the benefit of the NLP community.

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