Papers by Mohamed Nadif
More Discriminative Sentence Embeddings via Semantic Graph Smoothing (2024.eacl-short)
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| Challenge: | Text categorization is a natural language processing task that involves arranging texts into coherent groups based on their content. |
| Approach: | They propose to use semantic graph smoothing to enhance sentence embeddings from pretrained models to improve results for supervised and unsupervised document categorization tasks. |
| Outcome: | The proposed method improves sentences embeddings for supervised and unsupervised document categorization tasks. |
Is Anisotropy Truly Harmful? A Case Study on Text Clustering (2023.acl-short)
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| Challenge: | Contextualized pre-trained representations are widely used as input to various tasks such as information retrieval, anomaly detection and document clustering. |
| Approach: | They propose to examine the impact of different transformations on isotropy and performance to assess the true impact of anisotropi. |
| Outcome: | The proposed model is based on a clustering task and shows that it has limited impact on expressiveness and closeness. |
Unsupervised Anomaly Detection in Multi-Topic Short-Text Corpora (2023.eacl-main)
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| Challenge: | Unsupervised anomaly detection is a challenging task when the majority class is heterogeneous. |
| Approach: | They propose to use word embeddings to represent each sample by a dense vector and use a Mixture Model approach to detect which samples deviate the most from the underlying distributions of the corpus. |
| Outcome: | The proposed method is more efficient than state-of-the-art methods on real datasets. |