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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 151–160 of 247 papers

TitleStatusHype
Semantic Clustering and Convolutional Neural Network for Short Text Categorization—0
Semantic Similarity Computation for Abstract and Concrete Nouns Using Network-based Distributional Semantic Models—0
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding—0
Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization—0
Semi-supervised Text Categorization Using Recursive K-means Clustering—0
SemSim: Resources for Normalized Semantic Similarity Computation Using Lexical Networks—0
SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets—0
SenTube: A Corpus for Sentiment Analysis on YouTube Social Media—0
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization—0
Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention—0
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