32 citations · 37 across the 4 of their papers we have counts for
10 papers
Fairness-aware Class Imbalanced Learning
Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin +2
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the…
Learning Causal Bayesian Networks from Text
Farhad Moghimifar, Afshin Rahimi, Mahsa Baktashmotlagh +1
Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic…
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP
Fajri Koto, Afshin Rahimi, Jey Han Lau +1
Although the Indonesian language is spoken by almost 200 million people and the 10th most spoken language in the world, it is under-represented in NLP research. Previous work on In…
WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
Afshin Rahimi, Timothy Baldwin, Karin Verspoor
We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources. We propose a cross-lingual neural rera…
Massively Multilingual Transfer for NER
Afshin Rahimi, Yuan Li, Trevor Cohn
In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few…
Semi-supervised User Geolocation via Graph Convolutional Networks
Afshin Rahimi, Trevor Cohn, Timothy Baldwin
Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Netwo…
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
Afshin Rahimi, Timothy Baldwin, Trevor Cohn
We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presen…
A Neural Model for User Geolocation and Lexical Dialectology
Afshin Rahimi, Trevor Cohn, Timothy Baldwin
We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter…
Exploiting Text and Network Context for Geolocation of Social Media Users
Afshin Rahimi, Duy Vu, Trevor Cohn +1
Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very l…
Twitter User Geolocation Using a Unified Text and Network Prediction Model
Afshin Rahimi, Trevor Cohn, Timothy Baldwin
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements:(1) the removal of "celebrity" nodes to increase location homo…