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Text Categorization

Text Categorization is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of Text Categorization have been studied, each of which deals with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection, and sentiment classification to determine the sentiment typically in product or movie reviews.

Source: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Papers

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Showing 51–100 of 247 papers

TitleStatusHype
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing—0
Can characters reveal your native language? A language-independent approach to native language identification—0
Bridging Languages through Etymology: The case of cross language text categorization—0
A Proposal of Automatic Error Correction in Text—0
Categorization of Turkish News Documents with Morphological Analysis—0
Character-based recurrent neural networks for morphological relational reasoning—0
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words—0
Cluster Based Symbolic Representation for Skewed Text Categorization—0
BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function—0
A Practical Perspective on Latent Structured Prediction for Coreference Resolution—0
A Machine Learning Method to Distinguish Machine Translation from Human Translation—0
Complex Decomposition of the Negative Distance kernel—0
BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network—0
Bayesian Kernel Methods for Natural Language Processing—0
A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization—0
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification—0
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization—0
Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting—0
An Unsupervised Morphological Criterion for Discriminating Similar Languages—0
A Machine Learning Framework for Authorship Identification From Texts—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
Bag Reference Vector for Multi-instance Learning—0
BabelDomains: Large-Scale Domain Labeling of Lexical Resources—0
A Novel Term_Class Relevance Measure for Text Categorization—0
A Visual Quality Index for Fuzzy C-Means—0
Automatic Keyword Extraction on Twitter—0
A Novel Feature Selection and Extraction Technique for Classification—0
A Joint Segmentation and Classification Framework for Sentiment Analysis—0
Automatic Keyphrase Extraction: A Survey of the State of the Art—0
Digital Leafleting: Extracting Structured Data from Multimedia Online Flyers—0
Dense Components in the Structure of WordNet—0
Automatic Generation of Language-Independent Features for Cross-Lingual Classification—0
Annotation Artifacts in Natural Language Inference Data—0
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs—0
DISCO: A System Leveraging Semantic Search in Document Review—0
DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation—0
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning—0
Diversifying Neural Conversation Model with Maximal Marginal Relevance—0
Automatic Corpora Construction for Text Classification—0
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements—0
EEF: Exponentially Embedded Families with Class-Specific Features for Classification—0
Effect of small sample size on text categorization with support vector machines—0
Effects of term weighting approach with and without stop words removing on Arabic text classification—0
Efficient multivariate sequence classification—0
DeepPurple: Estimating Sentence Semantic Similarity using N-gram Regression Models and Web Snippets—0
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Fast Sampling for Bayesian Max-Margin Models—0
Feature Hashing for Language and Dialect Identification—0
Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison—0
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