Aicyber's System for NLPCC 2017 Shared Task 2: Voting of Baselines
Du Steven, Xi Zhang

TL;DR
This paper describes Aicyber's ensemble system for NLPCC 2017 shared task 2, combining deep learning models with traditional bag-of-word methods to improve performance.
Contribution
The paper introduces a novel voting ensemble that integrates deep learning and traditional models for Chinese text classification.
Findings
Voting ensemble outperforms individual models
Deep learning models leverage character-enhanced word vectors
Traditional bag-of-word model contributes to overall accuracy
Abstract
This paper presents Aicyber's system for NLPCC 2017 shared task 2. It is formed by a voting of three deep learning based system trained on character-enhanced word vectors and a well known bag-of-word model.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Text and Document Classification Technologies
