Abstract
We propose a simple yet effective text-based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.- Anthology ID:
- P17-2033
- 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:
- 209–216
- Language:
- URL:
- https://aclanthology.org/P17-2033
- DOI:
- 10.18653/v1/P17-2033
- Cite (ACL):
- Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2017. A Neural Model for User Geolocation and Lexical Dialectology. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 209–216, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Neural Model for User Geolocation and Lexical Dialectology (Rahimi et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/P17-2033.pdf