A Self-Training Approach for Short Text Clustering

2019-08-01 · WS 2019 8 · Amir Hadifar, Lucas Sterckx, Thomas Demeester, Chris Develder

Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations of the short texts. Low-dimensional continuous representations or embeddings can counter that sparseness problem: their high representational power is exploited in deep clustering algorithms. While deep clustering has been studied extensively in computer vision, relatively little work has focused on NLP. The method we propose, learns discriminative features from both an autoencoder and a sentence embedding, then uses assignments from a clustering algorithm as supervision to update weights of the encoder network. Experiments on three short text datasets empirically validate the effectiveness of our method.

📄 PDF Abstract BibTeX

Code (1)

hadifar/stc_clustering tf

Tasks

ClusteringDeep ClusteringSentenceSentence EmbeddingSentence-EmbeddingShort Text Clustering Text Clustering

Methods 이 논문이 사용한 방법론

Solana Customer Service Number +1-833-534-1729 설명 없음

Self-Taught Convolutional Neural Networks for Short Text Clustering

2017-01-01 · Jiaming Xu, Peng Wang, Suncong Zheng, Guanhua Tian 외

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which c…

Clustering Dimensionality Reduction Short Text Clustering Text Clustering+1

Self-supervised Document Clustering Based on BERT with Data Augment

2020-11-17 · Haoxiang Shi, Cen Wang

Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder…

Clustering Contrastive Learning Data Augmentation Text Clustering

Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering

2023-05-23 · Xiaolin Zheng, Mengling Hu, Weiming Liu, Chaochao Chen 외

Short text clustering is challenging since it takes imbalanced and noisy data as inputs. Existing approaches cannot solve this problem well, since (1) they are prone to obtain degenerate solutions especially on heavy imb…

Clustering Contrastive Learning Pseudo Label Representation Learning+2

Attentive Representation Learning with Adversarial Training for Short Text Clustering

2019-12-08 · Wei Zhang, Chao Dong, Jianhua Yin, Jianyong Wang

Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe s…

Clustering Information Retrieval Representation Learning Retrieval+2

Image Clustering Algorithm Based on Self-Supervised Pretrained Models and Latent Feature Distribution Optimization

2024-08-04 · Qiuyu Zhu, Liheng Hu, Sijin Wang

In the face of complex natural images, existing deep clustering algorithms fall significantly short in terms of clustering accuracy when compared to supervised classification methods, making them less practical. This pap…

Clustering Deep Clustering Image Clustering