Papers by Fabio A. González
A Deep Metric Learning Method for Biomedical Passage Retrieval (2020.coling-main)
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| Challenge: | Existing methods for passage retrieval are based on metric learning . the proposed approach is particularly well suited for domain-specific passage retrievals where it is very important to take into account different sources of information. |
| Approach: | They propose a method that learns a metric for questions and passages based on their internal semantic interactions. |
| Outcome: | The proposed method outperforms triplet loss and state-of-the-art methods in a biomedical passage retrieval task and outperformed triplet losses by 10% and 26%. |
Early Text Classification Using Multi-Resolution Concept Representations (N18-1)
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| Challenge: | e-communications have been misused by cyber-criminals, who hide in the depths of the web. |
| Approach: | They propose a document representation which allows us to generate multiple "views" of the analyzed text. |
| Outcome: | The proposed representation outperforms existing models in two tasks where anticipation is critical: sexual predator detection and depression detection. |
A Genre-Aware Attention Model to Improve the Likability Prediction of Books (D18-1)
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| Challenge: | Existing methods for likability prediction are time-consuming and too rigid. |
| Approach: | They propose a novel neural architecture that incorporates genre supervision to assign weights to individual feature types based on the characteristics of each book. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves competitive results. |
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (N18-2)
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| Challenge: | We obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
| Approach: | They propose to model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. |
| Outcome: | The proposed model obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |