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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

Showing 8190 of 247 papers

TitleStatusHype
Annotation Artifacts in Natural Language Inference Data0
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs0
DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation0
Digital Leafleting: Extracting Structured Data from Multimedia Online Flyers0
DISCO: A System Leveraging Semantic Search in Document Review0
Automatic Corpora Construction for Text Classification0
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning0
Diversifying Neural Conversation Model with Maximal Marginal Relevance0
DeepPurple: Estimating Sentence Semantic Similarity using N-gram Regression Models and Web Snippets0
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data0
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