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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 151–175 of 247 papers

TitleStatusHype
Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders—0
Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification—0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis—0
Interactive Tools and Tasks for the Hebrew Bible—0
Irregularity Detection in Categorized Document Corpora—0
Is it Useful to Support Users with Lexical Resources? A User Study.—0
JaTeCS an open-source JAva TExt Categorization System—0
KeLP: a Kernel-based Learning Platform for Natural Language Processing—0
langid.py: An Off-the-shelf Language Identification Tool—0
Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models—0
Latent Semantic Matching: Application to Cross-language Text Categorization without Alignment Information—0
Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization—0
Learning Non-Linear Functions for Text Classification—0
Learning Timeline Difference for Text Categorization—0
Learning with Inadequate and Incorrect Supervision—0
Linguistic Structured Sparsity in Text Categorization—0
Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance—0
Linking, Searching, and Visualizing Entities in Wikipedia—0
Machine-guided Solution to Mathematical Word Problems—0
Machine learning approach for text and document mining—0
Mapping WordNet Domains, WordNet Topics and Wikipedia Categories to Generate Multilingual Domain Specific Resources—0
MaxMin Linear Initialization for Fuzzy C-Means—0
Measuring Term Informativeness in Context—0
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks—0
Modeling Topic Dependencies in Hierarchical Text Categorization—0
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