Papers with topic inference
CASE: Large Scale Topic Exploitation for Decision Support Systems (2025.coling-demos)
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| Challenge: | Topic models are still a major tool for information retrieval and summarization, but their integration into decision-making systems is limited. |
| Approach: | They propose a tool for exploiting topic information for semantic analysis of large corpora using a Solr engine and a customized indexing strategy. |
| Outcome: | The proposed approach can be used to analyze large corpora and perform thematic trend analysis, topic-based document retrieval, or similarity search. |
Neural Topic Modeling with Bidirectional Adversarial Training (2020.acl-main)
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| Challenge: | Recent studies have shown that neural topic models for automatic topic extraction avoid complicated mathematical derivations for model inference. |
| Approach: | They propose a bidirectional adversarial topic model which uses a generator and an encoder to infer topic distribution. |
| Outcome: | The proposed model outperforms baselines and competitive models in three benchmark corpora. |
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)
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| Challenge: | Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources . |
| Approach: | They propose a domain adaptation framework that disentangles domaininvariant and domain-specific components to improve topic adaptation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on low-resource datasets on diverse datasets. |
Topic Memory Networks for Short Text Classification (D18-1)
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| Challenge: | Existing classification models for short texts are weak due to data sparsity . |
| Approach: | They propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. |
| Outcome: | The proposed model outperforms state-of-the-art models on short text classification, while generating coherent topics. |
CEMTM: Contextual Embedding-based Multimodal Topic Modeling (2025.emnlp-main)
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| Challenge: | CEMTM is a context-enhanced multimodal topic model that can infer coherent topic structures from documents . traditional multimodal topics failed to capture deeper cross-modal interactions . large vision language models (LLMs) and LVLMs have shown remarkable capacity to encode rich semantic knowledge from vast corpora. |
| Approach: | They propose a context-enhanced multimodal topic model that uses tokens to weight contributions to topic inference. |
| Outcome: | The proposed model outperforms unimodal and multimodal benchmarks on six multimodal domains and captures semantics in scientific articles. |