{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/end-to-end-network-for-twitter-geolocation","title":"End-to-end Network for Twitter Geolocation Prediction and Hashing","arxiv_id":"1710.04802","date":"2017-10-13","proceeding":"IJCNLP 2017 11","authors":["Jey Han Lau","Lianhua Chi","Khoi-Nguyen Tran","Trevor Cohn"],"abstract":"We propose an end-to-end neural network to predict the geolocation of a\ntweet. The network takes as input a number of raw Twitter metadata such as the\ntweet message and associated user account information. Our model is language\nindependent, and despite minimal feature engineering, it is interpretable and\ncapable of learning location indicative words and timing patterns. Compared to\nstate-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we\npropose extensions to the model to compress representation learnt by the\nnetwork into binary codes. Experiments show that it produces compact codes\ncompared to benchmark hashing algorithms. An implementation of the model is\nreleased publicly.","url_abs":"http://arxiv.org/abs/1710.04802v1","url_pdf":"http://arxiv.org/pdf/1710.04802v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"end-to-end-network-for-twitter-geolocation","repo_url":"https://github.com/jhlau/twitter-deepgeo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}