Papers by Carlos Aguirre
Do Models of Mental Health Based on Social Media Data Generalize? (2020.findings-emnlp)
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| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
| Approach: | They explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. |
| Outcome: | The proposed methods show that they can be used to train and analyze large datasets and that they are robust to large dataset sizes. |
Gender and Racial Fairness in Depression Research using Social Media (2021.eacl-main)
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| Challenge: | Existing studies show that social media behavior can indicate mental health of an individual . previous studies have raised concerns about possible biases in models produced from such data, but no study has investigated how these biase recur with demographic groups. |
| Approach: | They analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial/ethnic demographic groups. |
| Outcome: | The proposed model performs better for gender and racial/ethnic groups than other models and provides recommendations on how to avoid biases in future research. |
Bernice: A Multilingual Pre-trained Encoder for Twitter (2022.emnlp-main)
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| Challenge: | Existing language models for Twitter are monolingual, adapted from other domains, or trained on limited amount of in-domain data. |
| Approach: | They propose a multilingual RoBERTa language model that is trained from scratch on 2.5 billion tweets with a custom tweet-focused tokenizer. |
| Outcome: | The proposed model outperforms or matches models trained on monolingual and multilingual tweets on a variety of benchmarks and is more efficient compute- and data-wise to train completely on in-domain data with a specialized domain-specific tokenizer. |
PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature (2022.aacl-demo)
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| Challenge: | Existing literature search engines cannot deliver recipe steps of the literature . manual processing and assimilating useful information is expensive and time-consuming for researchers. |
| Approach: | They propose a machine learning-based procedural information extraction and knowledge management system that extracts procedural recipe steps, figures, and tables from materials science articles. |
| Outcome: | The proposed system extracts procedural information recipe steps, figures, and tables from materials science articles and provides information retrieval capability and statistics visualization functionality. |