Lianhua Chi


End-to-end Network for Twitter Geolocation Prediction and Hashing
Jey Han Lau | Lianhua Chi | Khoi-Nguyen Tran | Trevor Cohn
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we propose extensions to the model to compress representation learnt by the network into binary codes. Experiments show that it produces compact codes compared to benchmark hashing algorithms. An implementation of the model is released publicly.


Temporal Modelling of Geospatial Words in Twitter
Bo Han | Antonio Jimeno Yepes | Andrew MacKinlay | Lianhua Chi
Proceedings of the Australasian Language Technology Association Workshop 2016

Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features
Lianhua Chi | Kwan Hui Lim | Nebula Alam | Christopher J. Butler
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task “Geolocation Prediction in Twitter” of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, #hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, #hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.