Overcoming the Long Tail Problem: A Case Study on CO2-Footprint Estimation of Recipes using Information Retrieval - ACL Anthology

This is an internal, incomplete preview of a proposed change to the ACL Anthology. For efficiency reasons, we generate only three BibTeX files per volume, and the preview may be incomplete in other ways, or contain mistakes. Do not treat this content as an official publication.

Overcoming the Long Tail Problem: A Case Study on CO2-Footprint Estimation of Recipes using Information Retrieval

Melanie Geiger, Martin Braschler

Anthology ID:: L18-1568
Volume:: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Month:: May
Year:: 2018
Address:: Miyazaki, Japan
Editors:: Nicoletta Calzolari, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, Takenobu Tokunaga
Venue:: LREC
SIG:
Publisher:: European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:: https://aclanthology.org/L18-1568
DOI:
Bibkey:
Cite (ACL):: Melanie Geiger and Martin Braschler. 2018. Overcoming the Long Tail Problem: A Case Study on CO2-Footprint Estimation of Recipes using Information Retrieval. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
Cite (Informal):: Overcoming the Long Tail Problem: A Case Study on CO2-Footprint Estimation of Recipes using Information Retrieval (Geiger & Braschler, LREC 2018)
Copy Citation:
PDF:: https://preview.aclanthology.org/landing_page/L18-1568.pdf