Discriminating between Similar Languages Using a Combination of Typed and Untyped Character N-grams and Words

Helena Gomez, Ilia Markov, Jorge Baptista, Grigori Sidorov, David Pinto


Abstract
This paper presents the cic_ualg’s system that took part in the Discriminating between Similar Languages (DSL) shared task, held at the VarDial 2017 Workshop. This year’s task aims at identifying 14 languages across 6 language groups using a corpus of excerpts of journalistic texts. Two classification approaches were compared: a single-step (all languages) approach and a two-step (language group and then languages within the group) approach. Features exploited include lexical features (unigrams of words) and character n-grams. Besides traditional (untyped) character n-grams, we introduce typed character n-grams in the DSL task. Experiments were carried out with different feature representation methods (binary and raw term frequency), frequency threshold values, and machine-learning algorithms – Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB). Our best run in the DSL task achieved 91.46% accuracy.
Anthology ID:
W17-1217
Volume:
Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)
Month:
April
Year:
2017
Address:
Valencia, Spain
Venues:
VarDial | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
137–145
Language:
URL:
https://aclanthology.org/W17-1217
DOI:
10.18653/v1/W17-1217
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W17-1217.pdf