SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification

doi 10.18653/v1/s16-1007
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Association for Computational Linguistics

Related search

ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis Using Deep Learning

2017English

DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

2017English

Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter

2017English

NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: A Simple Ensemble Method With Different Embeddings

2017English

LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words for English and Arabic Tweet Polarity Classification

2017English

AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis

2016English

Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification

2017English

INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-Of-Embedding Words

2016English

Emotion Classification of Restaurant and Laptop Review Dataset: Semeval 2014 Task 4

International Journal of Computer Applications
2015English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy