Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class
Not on the map. Why?
Abstract (arXiv)
Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performance improvements, supervised few-shot methods, combined with a simple topic extraction method pose a significant challenge to unsupervised topic modeling methods. Our research shows that supervised few-shot learning, combined with a simple topic extraction method, can outperform unsupervised topic modeling techniques in terms of generating coherent topics, even when only a few labeled documents per class are used.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 19 Dec 2022.
Builds on
18 of 49 references.
References · 18
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2014Distributed Representations of Sentences and Documentscited by 677
- 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
- 2019Topic Modeling in Embedding Spacescited by 224
- 2017Autoencoding Variational Inference For Topic Modelscited by 168
- 2022Efficient Few-Shot Learning Without Promptscited by 135
- 2020Top2Vec: Distributed Representations of Topicscited by 124
6 more
- 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
- 2017Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a surveycited by 90
- 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
- 2020Cross-lingual Contextualized Topic Models with Zero-shot Learningcited by 66
- 2018ATM:Adversarial-neural Topic Modelcited by 23
- 2021Few-shot Learning for Topic Modelingcited by 5
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 2.
3 papers.