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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 76–100 of 247 papers

TitleStatusHype
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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