More Discriminative Sentence Embeddings via Semantic Graph Smoothing
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913237635694592 |
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| author | Fettal, Chakib Labiod, Lazhar Nadif, Mohamed |
| author_facet | Fettal, Chakib Labiod, Lazhar Nadif, Mohamed |
| contents | This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentence embeddings obtained from pretrained models to improve results for the text clustering and classification tasks. Our method, validated on eight benchmarks, demonstrates consistent improvements, showcasing the potential of semantic graph smoothing in improving sentence embeddings for the supervised and unsupervised document categorization tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12890 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | More Discriminative Sentence Embeddings via Semantic Graph Smoothing Fettal, Chakib Labiod, Lazhar Nadif, Mohamed Computation and Language Machine Learning This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentence embeddings obtained from pretrained models to improve results for the text clustering and classification tasks. Our method, validated on eight benchmarks, demonstrates consistent improvements, showcasing the potential of semantic graph smoothing in improving sentence embeddings for the supervised and unsupervised document categorization tasks. |
| title | More Discriminative Sentence Embeddings via Semantic Graph Smoothing |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.12890 |