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Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks
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Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies ...
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Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction ...
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Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection ...
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Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge ...
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Topic-driven and knowledge-aware transformer for dialogue emotion detection
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A disentangled adversarial neural topic model for separating opinions from plots in user reviews
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Multi-modal sarcasm detection with interactive in-modal and cross-modal graphs
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A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews ...
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CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering ...
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Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis
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Commonsense knowledge enhanced memory network for stance classification
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Transition-based directed graph construction for emotion-cause pair extraction
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A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis ...
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Abstract:
Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperform ing a number of competitive baselines by at least 2.08% in F-measure. ...
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URL: https://zenodo.org/record/3632943 https://dx.doi.org/10.5281/zenodo.3632943
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A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis ...
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TDAM: a topic-dependent attention model for sentiment analysis
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A knowledge regularized hierarchical approach for emotion cause analysis
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A Question Answering Approach to Emotion Cause Extraction ...
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