Text Categorization
41 papers with code • 0 benchmarks • 6 datasets
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
Benchmarks
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Libraries
Use these libraries to find Text Categorization models and implementations
Multilingual Reuters
DAWT
MNAD
Latest papers with no code
Monitoring Energy Trends through Automatic Information Extraction
Energy research is of crucial public importance but the use of computer science technologies like automatic text processing and data management for the energy domain is still rare.
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization
The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability.
Using Word Embeddings for Italian Crime News Categorization
The scope is the categorization of the news articles based on the type of crime they report.
A Proposal of Automatic Error Correction in Text
The great amount of information that can be stored in electronic media is growing up daily.
A comparison of latent semantic analysis and correspondence analysis of document-term matrices
In this article, we present a theoretical analysis and comparison of the two techniques in the context of document-term matrices.
BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function
This paper proposes an automatic Chinese text categorization method for solving the emergency event report classification problem.
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set
Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools.
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
Specifically, we jointly train two modules with different inductive biases -- a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning.
Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification
We obtained results analyzing the text and images separately, and also in combination.
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
