H
Heng Yang
Researcher at South China Normal University
Publications - 16
Citations - 256
Heng Yang is an academic researcher from South China Normal University. The author has contributed to research in topics: Computer science & Sentiment analysis. The author has an hindex of 3, co-authored 10 publications receiving 79 citations.
Papers
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Journal ArticleDOI
LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification
TL;DR: A Local Context Focus (LCF) mechanism is proposed for aspect-based sentiment classification based on Multi-head Self-Attention (MHSA), and utilizes the Context features Dynamic Mask (CDM) and Context Features Dynamic Weighted (CDW) layers to pay more attention to the local context words.
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A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction
TL;DR: A multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC, which equips the capability of extracting aspect term and inferring aspect term polarity synchronously and is effective to analyze both Chinese and English comments simultaneously.
Posted Content
A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction
TL;DR: Based on the local context focus (LCF) mechanism, this article proposed a multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC.
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Combining dynamic local context focus and dependency cluster attention for aspect-level sentiment classification
TL;DR: This article propose a concept of dependency cluster and design two modules named Dynamic Local Context Focus (DLCF) and Dependency Cluster Attention (DCA) respectively, which can dynamically capture the range of local context based on the different max distance from the target aspect term to its context words and allow the model to pay more attention to the cluster which is more critical for sentiment classification.
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Multifeature Interactive Fusion Model for Aspect-Based Sentiment Analysis
TL;DR: This work proposes a multifeature interactive fusion model for aspect-based sentiment analysis that has a better performance compared with the baseline models and applies the attention mechanism to calculate fusion weight of features, so that the key features information plays a more significant role in the sentiment analysis.