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.
@InProceedings{AHMIA18.832, author = {Oussama Ahmia and Nicolas Béchet and Pierre-François Marteau}, title = "{Two Multilingual Corpora Extracted from the Tenders Electronic Daily for Machine Learning and Machine Translation Applications.}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper