More Discriminative Sentence Embeddings via Semantic Graph Smoothing
Chakib Fettal Lazhar Labiod Mohamed Nadif
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.