Abstract
This paper describes the system developed by the Centre for English Corpus Linguistics (CECL) to discriminating similar languages, language varieties and dialects. Based on a SVM with character and POStag n-grams as features and the BM25 weighting scheme, it achieved 92.7% accuracy in the Discriminating between Similar Languages (DSL) task, ranking first among eleven systems but with a lead over the next three teams of only 0.2%. A simpler version of the system ranked second in the German Dialect Identification (GDI) task thanks to several ad hoc postprocessing steps. Complementary analyses carried out by a cross-validation procedure suggest that the BM25 weighting scheme could be competitive in this type of tasks, at least in comparison with the sublinear TF-IDF. POStag n-grams also improved the system performance.- Anthology ID:
- W17-1214
- Volume:
- Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Preslav Nakov, Marcos Zampieri, Nikola Ljubešić, Jörg Tiedemann, Shevin Malmasi, Ahmed Ali
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 115–123
- Language:
- URL:
- https://aclanthology.org/W17-1214
- DOI:
- 10.18653/v1/W17-1214
- Cite (ACL):
- Yves Bestgen. 2017. Improving the Character Ngram Model for the DSL Task with BM25 Weighting and Less Frequently Used Feature Sets. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 115–123, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Improving the Character Ngram Model for the DSL Task with BM25 Weighting and Less Frequently Used Feature Sets (Bestgen, VarDial 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W17-1214.pdf