More Discriminative Sentence Embeddings via Semantic Graph Smoothing

arXiv:2402.12890v1 Announce Type: cross
Abstract: 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.

abstract arxiv benchmarks classification clustering consistent cs.cl cs.lg embeddings fashion graph improvements learn learn more paper pretrained models semantic tasks text type unsupervised via