activity
20162020
most citedSurvey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective

3 citations · 6 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CL2020

DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains

Chang Xu, Cecile Paris, Surya Nepal +3

We address the issue of having a limited number of annotations for stance classification in a new domain, by adapting out-of-domain classifiers with domain adaptation. Existing app…

cs.CL2019

Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection

Adith Iyer, Aditya Joshi, Sarvnaz Karimi +2

Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work mentions errors in this prediction when symptom…

cs.CL20191 cited

A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics

Aditya Joshi, Sarvnaz Karimi, Ross Sparks +2

Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as cont…

cs.CL20191 cited

Recognising Agreement and Disagreement between Stances with Reason Comparing Networks

Chang Xu, Cecile Paris, Surya Nepal +1

We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialog…

cs.CL20193 cited

Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective

Aditya Joshi, Sarvnaz Karimi, Ross Sparks +2

Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of informa…

cs.CL2018

Cross-Target Stance Classification with Self-Attention Networks

Chang Xu, Cecile Paris, Surya Nepal +1

In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this wo…