Abstract
We evaluate feature hashing for language identification (LID), a method not previously used for this task. Using a standard dataset, we first show that while feature performance is high, LID data is highly dimensional and mostly sparse (>99.5%) as it includes large vocabularies for many languages; memory requirements grow as languages are added. Next we apply hashing using various hash sizes, demonstrating that there is no performance loss with dimensionality reductions of up to 86%. We also show that using an ensemble of low-dimension hash-based classifiers further boosts performance. Feature hashing is highly useful for LID and holds great promise for future work in this area.- Anthology ID:
- P17-2063
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 399–403
- Language:
- URL:
- https://aclanthology.org/P17-2063
- DOI:
- 10.18653/v1/P17-2063
- Cite (ACL):
- Shervin Malmasi and Mark Dras. 2017. Feature Hashing for Language and Dialect Identification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 399–403, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Feature Hashing for Language and Dialect Identification (Malmasi & Dras, ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/P17-2063.pdf