# Text and Document Classification Technologies

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/text-and-document-classification-technologies/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of machine learning algorithms for multi-label text classification, with an emphasis on techniques such as feature selection, Naive Bayes classifier, K-nearest Neighbor (KNN), hierarchical classification, and support vector machines (SVM). The research covers various aspects of document categorization and information retrieval in the context of text mining and natural language processing. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11550 |
| Works | 85 |

## Topic papers all

Showing 15 of 85.

- [Selecting critical features for data classification based on machine learning methods](https://scholariq.org/papers/selecting-critical-features-for-data-classification-based-on-machine-learning/)
- [A Decade Survey of Transfer Learning (2010–2020)](https://scholariq.org/papers/a-decade-survey-of-transfer-learning-2010-2020/)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://scholariq.org/papers/luke-deep-contextualized-entity-representations-with-entity-aware-self-attention/)
- [Chunking with support vector machines](https://scholariq.org/papers/chunking-with-support-vector-machines/)
- [Japanese dependency analysis using cascaded chunking](https://scholariq.org/papers/japanese-dependency-analysis-using-cascaded-chunking/)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://scholariq.org/papers/hdltex-hierarchical-deep-learning-for-text-classification/)
- [Incorporating domain knowledge into topic modeling via Dirichlet Forest priors](https://scholariq.org/papers/incorporating-domain-knowledge-into-topic-modeling-via-dirichlet-forest-priors/)
- [Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews](https://scholariq.org/papers/senti-lexicon-and-improved-naive-bayes-algorithms-for-sentiment-analysis-of/)
- [Use of support vector learning for chunk identification](https://scholariq.org/papers/use-of-support-vector-learning-for-chunk-identification/)
- [Ridge Regression, Hubness, and Zero-Shot Learning](https://scholariq.org/papers/ridge-regression-hubness-and-zero-shot-learning/)
- [Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition](https://scholariq.org/papers/dive-into-ambiguity-latent-distribution-mining-and-pairwise-uncertainty/)
- [Efficient English text classification using selected Machine Learning Techniques](https://scholariq.org/papers/efficient-english-text-classification-using-selected-machine-learning-techniques/)
- [The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters](https://scholariq.org/papers/the-effective-use-of-the-one-class-svm-classifier-for-handwritten-signature/)
- [Fast methods for kernel-based text analysis](https://scholariq.org/papers/fast-methods-for-kernel-based-text-analysis/)
- [Semi-supervised Multi-label Learning by Solving a Sylvester Equation](https://scholariq.org/papers/semi-supervised-multi-label-learning-by-solving-a-sylvester-equation/)

## Topic primary papers

Showing 15 of 17.

- [HDLTex: Hierarchical Deep Learning for Text Classification](https://scholariq.org/papers/hdltex-hierarchical-deep-learning-for-text-classification/)
- [Efficient English text classification using selected Machine Learning Techniques](https://scholariq.org/papers/efficient-english-text-classification-using-selected-machine-learning-techniques/)
- [Semi-supervised Multi-label Learning by Solving a Sylvester Equation](https://scholariq.org/papers/semi-supervised-multi-label-learning-by-solving-a-sylvester-equation/)
- [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning](https://scholariq.org/papers/contrastive-and-generative-graph-convolutional-networks-for-graph-based-semi/)
- [Multi-label learning with label-specific features by resolving label correlations](https://scholariq.org/papers/multi-label-learning-with-label-specific-features-by-resolving-label/)
- [Discriminative and Correlative Partial Multi-Label Learning](https://scholariq.org/papers/discriminative-and-correlative-partial-multi-label-learning/)
- [Feature-Induced Partial Multi-label Learning](https://scholariq.org/papers/feature-induced-partial-multi-label-learning/)
- [Mutual information based multi-label feature selection via constrained convex optimization](https://scholariq.org/papers/mutual-information-based-multi-label-feature-selection-via-constrained-convex/)
- [Label distribution learning with label-specific features](https://scholariq.org/papers/label-distribution-learning-with-label-specific-features/)
- [Hybrid Noise-Oriented Multilabel Learning](https://scholariq.org/papers/hybrid-noise-oriented-multilabel-learning/)
- [Label Error Correction and Generation through Label Relationships](https://scholariq.org/papers/label-error-correction-and-generation-through-label-relationships/)
- [Adaptive Metric Learning Vector Quantization for Ordinal Classification](https://scholariq.org/papers/adaptive-metric-learning-vector-quantization-for-ordinal-classification/)
- [Tournament Structure Ranking Techniques for Bayesian Text Classification with Highly Similar Categories](https://scholariq.org/papers/tournament-structure-ranking-techniques-for-bayesian-text-classification-with/)
- [LDA Based Feature Selection for Document Clustering](https://scholariq.org/papers/lda-based-feature-selection-for-document-clustering/)
- [Textual Inference with Deep Learning Technique](https://scholariq.org/papers/textual-inference-with-deep-learning-technique/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.