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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 221–230 of 247 papers

TitleStatusHype
DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation—0
Negative Deceptive Opinion Spam—0
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements—0
The Story of the Characters, the DNA and the Native Language—0
Measuring Term Informativeness in Context—0
From high heels to weed attics: a syntactic investigation of chick lit and literature—0
Feature Selection Based on Term Frequency and T-Test for Text Categorization—0
Semantic Similarity Computation for Abstract and Concrete Nouns Using Network-based Distributional Semantic Models—0
DeepPurple: Estimating Sentence Semantic Similarity using N-gram Regression Models and Web Snippets—0
Graph-based Semi-Supervised Learning Algorithms for NLP—0
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