Abstract
Semantic similarity is an area of Natural Language Processing that is useful for several downstream applications, such as machine translation, natural language generation, information retrieval, or question answering. The task consists in assessing the extent to which two sentences express or do not express the same meaning. To do so, corpora with graded pairs of sentences are required. The grade is positioned on a given scale, usually going from 0 (completely unrelated) to 5 (equivalent semantics). In this work, we introduce such a corpus for French, the first that we know of. It is comprised of 1,010 sentence pairs with grades from five annotators. We describe the annotation process, analyse these data, and perform a few experiments for the automatic grading of semantic similarity.- Anthology ID:
- 2020.lrec-1.851
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6889–6894
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.851
- DOI:
- Cite (ACL):
- Rémi Cardon and Natalia Grabar. 2020. A French Corpus for Semantic Similarity. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6889–6894, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A French Corpus for Semantic Similarity (Cardon & Grabar, LREC 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.lrec-1.851.pdf