More Discriminative Sentence Embeddings via Semantic Graph Smoothing
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 20 Feb 2024.
Builds on
12 of 25 references.
References · 12
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,107
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,086
- 2019Simplifying Graph Convolutional Networkscited by 1,474
- 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
- 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 991
- 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 411
- 2018Graph Convolutional Networks for Text Classificationcited by 345
- 2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
- 2021Dissecting the Diffusion Process in Linear Graph Convolutional Networkscited by 58
Built on by
Citing papers per year: 2026: 1.
1 paper.