Summary of the paper
| Title | From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers |
|---|---|
| Authors | Ingrid Falk, Delphine Bernhard and 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. |
| Topics | Statistical and Machine Learning Methods, Corpus (Creation, Annotation, etc.) |
Full paper  |
From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers |
| Bibtex | @InProceedings{FALK14.288, author = {Ingrid Falk and Delphine Bernhard and Christophe Gérard}, title = {From Non Word to New Word: Automatically Identifying Neologisms in French Newspapers}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |