activity
20192022
most citedPipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

13 citations · 14 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20221 cited

Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics

Carlos Aguirre, Mark Dredze, Philip Resnik

Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms acr…

cs.CL2021

Gender and Racial Fairness in Depression Research using Social Media

Carlos Aguirre, Keith Harrigian, Mark Dredze

Multiple studies have demonstrated that behavior on internet-based social media platforms can be indicative of an individual's mental health status. The widespread availability of…

cs.CL2020

On the State of Social Media Data for Mental Health Research

Keith Harrigian, Carlos Aguirre, Mark Dredze

Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain, in…

cs.IR201913 cited

Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

Huichen Yang, Carlos A. Aguirre, Maria F. De La Torre +10

This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions t…

cs.IR2019

A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams

Avishek Bose, Vahid Behzadan, Carlos Aguirre +1

We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (mar…