Papers by Yuyin Lu
Lifelong Learning of Topics and Domain-Specific Word Embeddings (2021.findings-acl)
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| Challenge: | Existing lifelong topic models focus on indomain text streams in which each chunk only contains documents from a single domain. |
| Approach: | They develop a lifelong collaborative model that uses non-negative matrix factorization to learn topics and domain-specific word embeddings. |
| Outcome: | The proposed model can learn topics and domain-specific word embeddings from a lifelong collaborative model. |
Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding (2024.lrec-main)
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| Challenge: | Existing hierarchical topic models are based on Euclidean space, which cannot retain the hierarchically semantic information in the corpus, leading to irrational structure of the generated topics. |
| Approach: | They propose a novel hierarchical topic model that uses contrastive learning to capture information from documents. |
| Outcome: | The proposed model performs on topic coherence and topic diversity, and on the rationality of the topic hierarchy. |
Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling (2023.acl-long)
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| Challenge: | Existing topic models assume that topics are independent and that they are not a tree structure, which complicates the analysis. |
| Approach: | They propose a neural topic model with a Gaussian mixture prior distribution to improve the model’s ability to adapt to sparse data. |
| Outcome: | The proposed model outperforms baseline models on sparse data on a set of widely used datasets and generates more coherent topics and rational topic structures. |
Graph-based Relation Mining for Context-free Out-of-vocabulary Word Embedding Learning (2023.acl-long)
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| Challenge: | Existing word embedding methods fail to model complex word formation well. |
| Approach: | They propose a graph-based relation mining method for OOV word embedding learning that can infer high-quality embeddables for OV words through passing and aggregating semantic attributes and relational information in the WRG. |
| Outcome: | The proposed method outperforms state-of-the-art models on both intrinsic and downstream tasks when faced with OOV words. |