Papers by Manuel V. Loureiro
SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels (2024.lrec-main)
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| Challenge: | Existing semantic textual similarity (STS) datasets are not suitable for news similarity detection due to their specificity to a single topic. |
| Approach: | They propose to segment news similarity datasets into topics to improve model training . they propose four different levels of complexity specifically designed for news similarities detection task . |
| Outcome: | The proposed dataset includes seven topics: Crime & Law, Culture & Entertainment, Disasters & Accidents, Economy & Business, Politics / Conflicts, Science & Technology, Sports. |
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks (2024.lrec-main)
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| Challenge: | Topic models aim to reveal latent structures within corpus of text through term-frequency statistics over bag-of-words representations. |
| Approach: | They propose to use bimodal vector representations of entities to extract latent representations from large language models and graph neural networks trained on symbolic relations to derive the most salient aspects of these conceptual units. |
| Outcome: | The proposed approach is better suited to working with entities than state-of-the-art models. |