Ash (Afshin) Rahimi
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 help machines understand conversational language
in Emergency Situations and Health Informatics.
- PhD position (closed): PhD project "NLP techniques for better access to Electronic Medical Records", joint project with me and Guido Zuccon. Application deadline is 30 November 2020.
- Two papers accepted to COLING2020, one on aligning Wikipedia and UMLS, and another on Indonesian pretrained models and benchmarks (see my GScholar).
- Paper with Gaurav Arora in ALTA2019 on catastrophic forgetting in NLP applications.
- I joined The University of Queensland as a lecturer (Assisstant Professor). If you're interested in working with me on NLP/Social Media/Health, please email me.
- There are more than 6000 languages without any annotation, our new ACL-19 paper on transfer learning (few shot and zero shot NER) for those languages+ slides .
- Geotagged data is scarce, our new ACL-18 paper on semi-supervised user geolocation using graph convolutional networks + slides .
- 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!