My research interests fall within the fields of Natural Language Processing, Social Network Analysis and Machine Learning.
I am specifically interested in exploiting both structured and unstructured data to get insights about people and their interests.
My PhD research was focused on combining text and network information in a semi-supervised setting to learn user demographics and
specifically Twitter user geolocation which resulted in state-of-the-art geolocation performance published in ACL, NAACL and EMNLP.
- Geotagged data is scarce, our new ACL2-18 paper on semi-supervised user geolocation using graph convolutional networks.
- Working on NLP for Low-resource languages with Trevor Cohn as a research fellow.
- I defended my PhD, and submitted my thesis. The slides are available here.
- Our EMNLP2017 paper on using Mixture Density Networks for geolocation and RBF networks for lexical dialectology is published. The code and slides are available.
- Our ACL 2017 paper on geolocation (predict location given text) and lexical dialectology (predict text given location) where we talk about utilising both network and text information for geolocation and also use the geolocation model to retrieve dialect words within U.S. is available. The code and the evaluation dataset can be found here. It was selected as one of the outstanding papers!
- The Youtube Demo and Web UI of our geolocation tool is ready to use. The corresponding paper is published at ACL 2016, demonstration papers.
- Our paper on geolocation of social media users is accepted at ACL 2015. We use social relationships and also tweet content of Twitter users to find where they live!
- Our paper on geolocation of social media users is accepted at NAACL 2015. We use social relationships and also tweet content of Twitter users to find where they live!