Chenhua Chen


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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