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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 141150 of 247 papers

TitleStatusHype
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs0
Using Linear Classifiers for the Automatic Triage of Posts in the 2016 CLPsych Shared Task0
UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization0
EEF: Exponentially Embedded Families with Class-Specific Features for Classification0
Improving Bilingual Terminology Extraction from Comparable Corpora via Multiple Word-Space Models0
Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting0
Interactive Tools and Tasks for the Hebrew Bible0
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings0
Massively Multilingual Word EmbeddingsCode0
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