Summary of More Discriminative Sentence Embeddings Via Semantic Graph Smoothing, by Chakib Fettal et al.
More Discriminative Sentence Embeddings via Semantic Graph Smoothing
by Chakib Fettal, Lazhar Labiod, Mohamed Nadif
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an empirical approach to learning more discriminative sentence representations without requiring labeled data. The authors build upon pre-trained models by applying semantic graph smoothing to enhance sentence embeddings, which leads to improved results in text clustering and classification tasks. By leveraging eight benchmark datasets, the method demonstrates consistent enhancements, highlighting the potential of semantic graph smoothing for supervised and unsupervised document categorization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make computers better at understanding sentences without being taught what they mean first. Researchers are trying to improve sentence representations, which is important for tasks like classifying documents or clustering similar texts. They found a new way to do this by smoothing out the meaning of words and phrases in sentences. This approach worked well on many different datasets, showing that it can be a useful tool for many applications. |
Keywords
* Artificial intelligence * Classification * Clustering * Supervised * Unsupervised
