From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers

Ingrid Falk, Delphine Bernhard, Christophe Gérard


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:
Bibkey:
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)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/288_Paper.pdf