Papers by Jihong Ouyang
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)
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| Challenge: | Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Approach: | They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Outcome: | Empirical results show that the proposed model can generate more coherent topics than baseline topic models. |
Semi-Supervised Text Classification with Balanced Deep Representation Distributions (2021.acl-long)
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| Challenge: | Semi-Supervised Text Classification (SSTC) is a type of self-training that uses labeled and unlabeled data to perform certain applications. |
| Approach: | They propose a method to initialize a deep classifier by training over labeled texts . they then alternatively predict unlabeled texts as their pseudo-labels and train them over the mixture . |
| Outcome: | Empirical results show that the proposed method is more accurate when labeled texts are scarce. |
Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)
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| Challenge: | Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training. |
| Approach: | They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation. |
| Outcome: | The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks. |