Papers by Christian Gomes
On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction (2021.findings-acl)
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| Challenge: | Neural network algorithms excel on Automatic Text Classification tasks, but they are expensive and require high computational costs. |
| Approach: | They propose to exploit the cost-effectiveness of stacking of automatic text classification classifiers to improve their effectiveness. |
| Outcome: | The proposed method can predict the best ensemble in each scenario using only fraction of available training data. |
CluHTM - Semantic Hierarchical Topic Modeling based on CluWords (2020.acl-main)
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| Challenge: | Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. |
| Approach: | They propose a hierarchical matrix factorization that exploits latent topics and relationships among them to create a powerful tool for data analysis and exploration. |
| Outcome: | The proposed method outperforms baselines and datasets in the vast majority of cases. |