Abstract
In this paper we present a statistical machine learning approach to formal neologism detection going some way beyond the use of exclusion lists. We explore the impact of three groups of features: form related, morpho-lexical and thematic features. The latter type of features has not yet been used in this kind of application and represents a way to access the semantic context of new words. The results suggest that form related features are helpful at the overall classification task, while morpho-lexical and thematic features better single out true neologisms.- Anthology ID:
- L14-1260
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 4337–4344
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/288_Paper.pdf
- DOI:
- Cite (ACL):
- Ingrid Falk, Delphine Bernhard, and Christophe Gérard. 2014. From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4337–4344, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers (Falk et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/288_Paper.pdf