Papers Semi-Supervised Text Classification
“Semi-Supervised Text Classification” 태그가 달린 논문 42편 · 필터 해제
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks, including citation network analysis or …
Semi-Supervised Text Classificationtext-classificationText ClassificationSemi-Supervised Text Classification via Self-Pretraining
We present a neural semi-supervised learning model termed Self-Pretraining. Our model is inspired by the classic self-training algorithm. However, as opposed to self-training, Self-Pretraining is threshold-free, it can p…
Pseudo LabelSemi-Supervised Text ClassificationText ClassificationFLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks
In natural language processing (NLP), state-of-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly red…
Semi-Supervised Text ClassificationText ClassificationSemi-Supervised Text Classification with Balanced Deep Representation Distributions
Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseud…
ClassificationSemi-Supervised Text Classificationtext-classificationText ClassificationA Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents
In the area of customer support, understanding customers' intents is a crucial step. Machine learning plays a vital role in this type of intent classification. In reality, it is typical to collect confirmation from custo…
Classificationintent-classificationMulti-Task LearningSemi-Supervised Text Classification+2Inductive Topic Variational Graph Auto-Encoder for Text Classification
Graph convolutional networks (GCNs) have been applied recently to text classification and produced an excellent performance. However, existing GCN-based methods do not assume an explicit latent semantic structure of docu…
ClassificationInductive LearningRepresentation LearningSemi-Supervised Text Classification+2Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
Although attention mechanisms have become fundamental components of deep learning models, they are vulnerable to perturbations, which may degrade the prediction performance and model interpretability. Adversarial trainin…
Semi-Supervised Text Classificationtext-classificationText ClassificationConsistency Training with Virtual Adversarial Discrete Perturbation
Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar. Previous studies have proposed various augmentation methods for the perturbation but are limited in that t…
Semi-Supervised Text ClassificationText ClassificationRevisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches. Several prior works have suggested that either complex pretraining schemes usin…
ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+5Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction…
ClassificationGeneral ClassificationManagementSemi-Supervised Text Classification+3MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by int…
ClassificationData AugmentationGeneral ClassificationSemi-Supervised Text Classification+1Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses
We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses. SMDA utilizes recent transformer-based models to encode each sent…
Data AugmentationSemi-Supervised Text ClassificationSentenceText Classification+1Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media
Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce the burden of creating extensive labeled…
General ClassificationSemi-Supervised Text Classificationtext-classificationText ClassificationSemi-Supervised Learning with Normalizing Flows
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an e…
image-classificationImage ClassificationSemi-Supervised Image ClassificationSemi-Supervised Text ClassificationVariational Pretraining for Semi-supervised Text Classification
We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, u…
ClassificationGeneral ClassificationSemi-Supervised Text ClassificationText ClassificationAdversarial Dropout for Recurrent Neural Networks
Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques for RNNs were introduced to respond to t…
Language ModelingLanguage ModellingSemi-Supervised Text ClassificationText ClassificationDelta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings
We propose a novel and simple method for semi-supervised text classification. The method stems from the hypothesis that a classifier with pretrained word embeddings always outperforms the same classifier with randomly in…
General ClassificationSemi-Supervised Text ClassificationSentiment AnalysisText ClassificationDid You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
Millions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, and other analyses of population health at global…
EpidemiologySemi-Supervised Text ClassificationText ClassificationDeconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. …
General ClassificationRepresentation LearningSemi-Supervised Text ClassificationText ClassificationAdversarial Training Methods for Semi-Supervised Text Classification
Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both metho…
ClassificationGeneral ClassificationSemi-Supervised Text ClassificationSentiment Analysis+2