Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning

• URL: http://arxiv.org/abs/2410.11355v1
• Date: Tue, 15 Oct 2024 07:25:33 GMT
• Title: Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning
• Authors: Minoo Jafarlou, Mario M. Kubek,
• Abstract summary: This study explores label propagation in semi-supervised learning. We employ a transductive label propagation method based on the manifold assumption for text classification. By extending labels based on cosine proximity within a nearest neighbor graph from network embeddings, we combine unlabeled data into supervised learning.
• Score: 0.0
• License:
• Abstract: Labeling datasets is a noteworthy challenge in machine learning, both in terms of cost and time. This research, however, leverages an efficient answer. By exploring label propagation in semi-supervised learning, we can significantly reduce the number of labels required compared to traditional methods. We employ a transductive label propagation method based on the manifold assumption for text classification. Our approach utilizes a graph-based method to generate pseudo-labels for unlabeled data for the text classification task, which are then used to train deep neural networks. By extending labels based on cosine proximity within a nearest neighbor graph from network embeddings, we combine unlabeled data into supervised learning, thereby reducing labeling costs. Based on previous successes in other domains, this study builds and evaluates this approach's effectiveness in sentiment analysis, presenting insights into semi-supervised learning.

Related papers

• Graph-Based Semi-Supervised Segregated Lipschitz Learning [0.21847754147782888]
  This paper presents an approach to semi-supervised learning for the classification of data using the Lipschitz Learning on graphs. We develop a graph-based semi-supervised learning framework that leverages the properties of the infinity Laplacian to propagate labels in a dataset where only a few samples are labeled.
  arXiv  Detail & Related papers  (2024-11-05T17:16:56Z)
• Posterior Label Smoothing for Node Classification [2.737276507021477]
  We propose a simple yet effective label smoothing for the transductive node classification task. We design the soft label to encapsulate the local context of the target node through the neighborhood label distribution. In the following analysis, we find that incorporating global label statistics in posterior computation is the key to the success of label smoothing.
  arXiv  Detail & Related papers  (2024-06-01T11:59:49Z)
• GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference [90.5402652758316]
  We propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net. It uses labeled information to guide the learning of unlabeled instances. It achieves competitive segmentation accuracy and significantly improves the mIoU by +7$%$ compared to previous approaches.
  arXiv  Detail & Related papers  (2021-12-28T06:48:03Z)
• GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference [153.354332374204]
  We propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net. We first introduce a feature alignment objective between labeled and unlabeled data to capture potentially similar image pairs. MITrans is shown to be a powerful knowledge module for further progressive refining features of unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks.
  arXiv  Detail & Related papers  (2021-06-29T02:48:45Z)
• Joining datasets via data augmentation in the label space for neural networks [6.036150783745836]
  We propose a new technique leveraging artificially created knowledge graph, recurrent neural networks and policy gradient that successfully achieve the dataset joining in the label space. Empirical results on both image and text classification justify the validity of our approach.
  arXiv  Detail & Related papers  (2021-06-17T06:08:11Z)
• Boosting Semi-Supervised Face Recognition with Noise Robustness [54.342992887966616]
  This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling. We develop a semi-supervised face recognition solution, named Noise Robust Learning-Labelling (NRoLL), which is based on the robust training ability empowered by GN.
  arXiv  Detail & Related papers  (2021-05-10T14:43:11Z)
• A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels [49.990938653249415]
  This research presents a methodology that assigns initial pseudo-labels to unlabeled data which is used as noisy-labeled data, and trains a deep neural network using the noisy-labeled data. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-03-08T11:46:02Z)
• Computationally Efficient Wasserstein Loss for Structured Labels [37.33134854462556]
  We propose a tree-Wasserstein distance regularized LDL algorithm, focusing on hierarchical text classification tasks. We show that the proposed method successfully considers the structure of labels during training, and it compares favorably with the Sinkhorn algorithm in terms of computation time and memory usage.
  arXiv  Detail & Related papers  (2021-03-01T10:45:13Z)
• PseudoSeg: Designing Pseudo Labels for Semantic Segmentation [78.35515004654553]
  We present a re-design of pseudo-labeling to generate structured pseudo labels for training with unlabeled or weakly-labeled data. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
  arXiv  Detail & Related papers  (2020-10-19T17:59:30Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.