Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction
Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma
Abstract
We propose a novel geolocation prediction model using a complex neural network. Geolocation prediction in social media has attracted many researchers to use information of various types. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8% increase in accuracy and a maximum of 6.6% increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.- Anthology ID:
- P17-1116
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1260–1272
- Language:
- URL:
- https://aclanthology.org/P17-1116
- DOI:
- 10.18653/v1/P17-1116
- Cite (ACL):
- Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, and Tomoko Ohkuma. 2017. Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1260–1272, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction (Miura et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/P17-1116.pdf