W-RNN improves text classification by extracting serialized text semantics.
problem Semantic constraint in sparse representation classification methods.
method Weighted RNN using word vectors and recurrent neural networks.
result W-RNN outperforms other methods in precision, recall, F1, and loss values.
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
Survey of text classification algorithms for complex documents.
problem Understanding and classifying complex texts using machine learning.
method Discusses various text feature extractions, dimensionality reduction methods, and classification algorithms.
result Overview of text classification techniques and their limitations.
Deep learning models outperform classical methods in text classification.
problem Improving text classification accuracy using deep learning.
method Comprehensive review of deep learning models and datasets for text classification.
result Deep learning models outperform classical methods on various text classification tasks.
Survey of deep learning for Hindi text classification.
problem Limited research on morphologically rich, low-resource Hindi text classification.
method Comparison of CNN, LSTM, Transformer, BERT, and LASER for Hindi text classification.
result Multilingual pre-trained sentence embeddings outperform traditional architectures for Hindi text classification.
Model uses RNN and CNN for sequence-based short-text classification.
problem Lack of sequence consideration in short-text classification.
method Recurrent and Convolutional Neural Networks for sequence data.
result Achieves state-of-the-art results on three datasets.
New method improves text classification using KL divergence.
problem Multinomial text classification problem
method Centroid estimation based on symmetric KL divergence
result Substantial improvements over traditional classifiers
OMP improves text classification accuracy with sparse models.
problem Overfitting in text classification due to high dimensionality.
method Greedy variable selection algorithm (OMP) and overlapping Group OMP.
result OMP and overlapping GOMP produce effective and very sparse models.
Study classifies poetry by poet using text categorization techniques.
problem Classifying poetry based on the poet who wrote it.
method Constructed a data set of English poetry, applied text categorization techniques, used Chi-Square for feature selection, and tested five classification algorithms.
result Sequential minimal optimization achieved a 70% classification success rate.
A graph model improves short text classification by integrating sentence relationships.
problem Sparse features in short text classification due to limited text length.
method PathWalk model combining graph networks and short sentences.
result PathWalk achieves state-of-the-art results on four datasets.
Developed text classification system for Azerbaijani language.
problem Text clustering problem in Azerbaijani language.
method Machine learning and embedding techniques.
result System successfully categorizes news, product reviews, and more.
Improved text classification using human-understandable features.
problem Text classification accuracy with traditional methods.
method Solicited human-comprehensible features from a teacher.
result Models with human-comprehensible features are competitive with traditional methods.
Enhances medical code predictions for multi-morbidity patients using text classification.
problem Improving accuracy in predicting medical codes for patients with multiple illnesses.
method Used machine learning techniques, including multi-label medical text classification, to enhance predictions.
result High dimensional embeddings pre-trained on health data significantly improve multi-label classification performance.
Combines BERT and graph CNN for improved text classification.
problem Text classification problems
method Combining BERT embedding and graph convolutional neural network
result Graph CNN model performs better than classical models combined with BERT
The paper classifies actions of a specific group on certain manifolds.
problem Classifying analytic actions of a specific semi-orthogonal group on manifolds.
method Adapting Uchida's construction, the paper explicitly constructs actions on specific manifolds and demonstrates that any action is covered by these.
result Any analytic action of the semi-orthogonal group on a closed, connected manifold is covered by the constructed actions.
Improved Naive Bayes for text classification with small datasets.
problem Poor performance of Naive Bayes in small training datasets.
method Introducing a correlation factor to Naive Bayes estimator.
result Our method achieves better accuracy than traditional Naive Bayes.
A new neural network for text classification reduces parameters with improved accuracy.
problem Reducing the number of parameters in text classification models.
method Compositional coding, capsule network, k-means routing algorithm.
result The proposed method achieves competitive accuracy with significantly fewer parameters.
Mitigates bias in text classification by weighting instances.
problem Unintended biases in text classification datasets based on demographic terms.
method Instance weighting to recover non-discrimination distribution.
result Effective mitigation of unintended biases without sacrificing generalization.
ML-Net improves multi-label text classification in biomedicine.
problem Challenges in multi-label text classification, especially in biomedicine.
method End-to-end deep learning framework combining label prediction and automated label count prediction.
result Significantly outperforms state-of-the-art methods in multi-label classification of biomedical texts.
Classifies CAD model descriptions and names from product websites.
problem Distinguishing product descriptions from other text and identifying product names.
method Paragraph vectors, character-level LSTM, word embeddings LSTM tagger.
result Promising results for distinguishing product descriptions and names.
Proposes a method to train deep neural networks with limited labeled data.
problem Lack of labeled data in neural text classification.
method Two modules: pseudo-document generator and self-training module.
result Significantly outperforms baseline methods without excessive labeled data.
HyperBERT enhances BERT for node classification on text-attributed hypergraphs.
problem Challenges in capturing hypergraph structure and text attributes in node classification.
method Mixing hypergraph-aware layers with BERT for improved node classification.
result HyperBERT achieves state-of-the-art results on text-attributed hypergraph benchmarks.
Dual adversarial co-learning improves multi-domain text classification.
problem Improving text classification across multiple domains.
method Dual adversarial co-learning with shared-private networks and dual adversarial regularizations.
result Achieves state-of-the-art performance on multi-domain sentiment classification datasets.
Neural network with loss ensemble improves text classification accuracy.
problem Improving text classification accuracy in noisy environments.
method Extended neural network with an ensemble loss function, weights tuned through gradient propagation.
result Improvement in classification accuracy and resilience against label noise.
PySS3 simplifies access to SS3's text classification and visualization.
problem Limited availability of an open-source SS3 implementation.
method Developed PySS3, an open-source Python package implementing SS3.
result PySS3 enables robust, explainable, and trusty text classification.
Enhanced word embeddings boost multiclass text classification accuracy.
problem Improving multiclass text classification accuracy using pre-trained embeddings.
method Proposed word-class embeddings (WCEs) to enhance pre-trained word embeddings.
result WCEs significantly improve multiclass text classification accuracy.
Study shows noisy historical data can still predict future text classification well.
problem Challenges in text classification with noisy, historical data.
method Examined how performance metrics on noisy data reflect future model performance.
result Noisy training data can be used to build effective prediction models for cleaner inputs.
Paper defends LSTM-based text classification models from backdoor attacks.
problem Backdoor attacks in LSTM models cause misclassification of spam or malicious speech.
method Backdoor Keyword Identification (BKI) to identify and exclude poisoned samples.
result BKI method effectively mitigates backdoor attacks in various text classification datasets.
New method uses word subspaces and term-frequency to improve text classification.
problem Lack of semantic meaning in bag-of-words features.
method Proposes word subspaces and term-frequency weighted word subspaces for text classification.
result Improved text classification performance compared to state-of-the-art algorithms.
Multimodal bitransformer boosts image-text classification.
problem Combining text and image modalities for improved classification.
method Supervised multimodal bitransformer model integrating text and image encoders.
result State-of-the-art performance on multimodal classification benchmarks.
Adversaries reprogram text classification models without changing the original network.
problem Reprogramming neural networks trained on discrete input spaces like text classification.
method Context-based vocabulary remapping model for white-box and black-box settings.
result Successfully repurposed various text-classification models for new tasks.
Text classification on drug SMILES strings yields competitive drug type classification results.
problem Classifying drug types using conventional text classification methods.
method Treated drug SMILES as sentences and applied basic NLP methods for classification.
result Competitive drug type classification results achieved.
New method predicts model performance change in text classification.
problem Data annotation bottleneck in text classification systems.
method Predicted Change of F Measure for stopping active learning.
result Estimates how much model performance changes at each iteration.
Combines BERT and GCN for better text classification.
problem Limited global information capture by BERT.
method Integrates BERT with VGCN for improved text classification.
result VGCN-BERT outperforms BERT and GCN alone.
The paper introduces metrics and methods to improve fairness in text classification models.
problem Counterfactual fairness issues in text classifiers, like predicting toxicity based on sensitive attributes.
method Developed a metric (CTF) and three approaches (blindness, counterfactual augmentation, CLP) to optimize counterfactual fairness during training.
result Blindness and CLP methods improve counterfactual fairness without harming classifier performance.
Enhanced metrics for multiclass classification improve on existing methods.
problem Lack of decisive poor classification results in existing multiclass metrics.
method Introduces three new metrics derived from multivariate Pearson correlation coefficients.
result New metrics decisively indicate poor classification results.
Efficiently predicts paths in hierarchical text classification using unlabeled data.
problem Costly labeling of documents in hierarchical text classification.
method Path cost-sensitive learning algorithm using generative model and path constraints.
result Significantly reduces computational cost and improves efficiency.
HAXMLNet tackles extreme multi-label text classification with hierarchical attention.
problem Tagging each text with relevant labels from an extreme-scale label set.
method Proposes a hierarchical structure with multi-label attention for efficient and effective XMTC.
result HAXMLNet achieves competitive performance compared to state-of-the-art methods.
Paper explores reducing precision in SVM for faster text classification.
problem Efficiency in multi-class text classification training.
method Comparison of SVM trained with reduced precision (16-bit, half) vs original.
result Reduced precision training maintains text classification accuracy.
TextNAS finds optimal text representation networks using neural architecture search.
problem Finding the optimal text representation networks is challenging.
method Proposes a novel search space for text representation and uses automatic neural architecture search.
result Automatic search discovers network architectures that outperform state-of-the-art models on text classification and natural language inference tasks.
Proposes a novel framework for multi-label text classification.
problem Lack of coherent consideration of non-consecutive and long-distance semantics and hierarchical relations among labels.
method Hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework.
result Significantly improves multi-label text classification performance.
Integrates global information into dropout for better text classification.
problem Improving neural networks for text classification.
method GI-Dropout, a novel dropout method integrating global information.
result Demonstrates the effectiveness of GI-Dropout on seven text classification tasks.
New method reduces text classification errors by learning writing style instead of content.
problem Deep neural networks learn superficial patterns specific to training data.
method Adversarial training to unlearn confounding features.
result Model generalizes better and learns writing style features.
Framework improves text classification under budget constraints.
problem Building robust text classifiers with limited computational resources.
method Jointly trains a selector to identify relevant words and passes them to a classifier, with a data aggregation scheme.
result Improves classifier performance and speeds up model with minimal accuracy loss.
Paper generates diverse, readable adversarial texts from scratch.
problem Text classification models are easily fooled by adversarial examples.
method Trained a conditional variational autoencoder (VAE) with adversarial loss and utilized GANs to generate consistent adversarial texts.
result Successfully generates adversarial texts with higher success rate and acceptable quality.
Debiasing techniques can worsen gender bias in text classification, but a tweak improves both.
problem Debiasing techniques can inadvertently increase gender bias in text classification.
method Investigated traditional debiasing techniques and found they worsen bias. Suggested a minor adjustment.
result A minor adjustment to debiasing techniques can reduce gender bias while maintaining high classification accuracy.
Paper tackles novelty detection in text classification.
problem Traditional text classification assumes known classes in testing, but often encounters unexpected instances.
method Converts problem to pair-wise matching, uses CNN with embedding matrices.
result Proposed method outperforms state-of-the-art baselines.
Active learning improves text classification performance by forecasting earlier.
problem Efficiently forecasting text classification model performance with active learning.
method Logarithmic models regressed on active learning data to forecast performance.
result F-measure is more difficult to forecast than accuracy.