@inproceedings{falk-etal-2014-non,
title = "From Non Word to New Word: Automatically Identifying Neologisms in {F}rench Newspapers",
author = "Falk, Ingrid and
Bernhard, Delphine and
G{\'e}rard, Christophe",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/288_Paper.pdf",
pages = "4337--4344",
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.",
}
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%0 Conference Proceedings
%T From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers
%A Falk, Ingrid
%A Bernhard, Delphine
%A Gérard, Christophe
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F falk-etal-2014-non
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/288_Paper.pdf
%P 4337-4344
Markdown (Informal)
[From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers](http://www.lrec-conf.org/proceedings/lrec2014/pdf/288_Paper.pdf) (Falk et al., LREC 2014)
ACL