Chenhua Chen


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Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis
Chenhua Chen | Zhiyang Teng | Zhongqing Wang | Yue Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dependency trees have been intensively used with graph neural networks for aspect-based sentiment classification. Though being effective, such methods rely on external dependency parsers, which can be unavailable for low-resource languages or perform worse in low-resource domains. In addition, dependency trees are also not optimized for aspect-based sentiment classification. In this paper, we propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees. To ease the learning of complicated structured latent variables, we build a connection between aspect-to-context attention scores and syntactic distances, inducing trees from the attention scores. Results on six English benchmarks and one Chinese dataset show that our model can achieve competitive performance and interpretability.


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Inducing Target-Specific Latent Structures for Aspect Sentiment Classification
Chenhua Chen | Zhiyang Teng | Yue Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Aspect-level sentiment analysis aims to recognize the sentiment polarity of an aspect or a target in a comment. Recently, graph convolutional networks based on linguistic dependency trees have been studied for this task. However, the dependency parsing accuracy of commercial product comments or tweets might be unsatisfactory. To tackle this problem, we associate linguistic dependency trees with automatically induced aspectspecific graphs. We propose gating mechanisms to dynamically combine information from word dependency graphs and latent graphs which are learned by self-attention networks. Our model can complement supervised syntactic features with latent semantic dependencies. Experimental results on five benchmarks show the effectiveness of our proposed latent models, giving significantly better results than models without using latent graphs.


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Global Methods for Cross-lingual Semantic Role and Predicate Labelling
Lonneke van der Plas | Marianna Apidianaki | Chenhua Chen
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Enhancing Active Learning for Semantic Role Labeling via Compressed Dependency Trees
Chenhua Chen | Alexis Palmer | Caroline Sporleder
Proceedings of 5th International Joint Conference on Natural Language Processing