Conference
Neural Topic Modeling via Contextual and Graph Information Fusion
| Τίτλος: | Neural Topic Modeling via Contextual and Graph Information Fusion |
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| Συγγραφείς: | Liu, Jiyuan, Yan, Jiaxing, Zhu, Chunjiang, Liu, Xingyu, Li, Qing, Rao, Yanghui |
| Πηγή: | Computer Science Faculty Publications |
| Στοιχεία εκδότη: | ODU Digital Commons |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | Old Dominion University: ODU Digital Commons |
| Θεματικοί όροι: | Graph theory—Data processing, Information storage and retrieval systems—Automatic indexing, Knowledge representation (Information theory), Machine learning—Statistical methods, Natural language processing (Computer science), Semantic networks (Information theory), Text mining, Topic modeling (Information retrieval), Artificial Intelligence and Robotics, Computer Sciences, Data Science |
| Περιγραφή: | Topic modeling is a powerful unsupervised tool for knowledge discovery. However, existing work struggles with generating limited-quality topics that are uninformative and incoherent, which hindering interpretable insights from managing textual data. In this paper, we improve the original variational autoencoder framework by incorporating contextual and graph information to address the above issues. First, the encoder utilizes topic fusion techniques to combine contextual and bag-of-words information well, and meanwhile exploits the constraints of topic alignment and topic sharpening to generate informative topics. Second, we develop a simple word co-occurrence graph information fusion strategy that efficiently increases topic coherence. On three benchmark datasets, our new framework generates more coherent and diverse topics compared to various baselines, and achieves strong performance on both automatic and manual evaluations. |
| Τύπος εγγράφου: | conference object |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | unknown |
| Relation: | https://digitalcommons.odu.edu/computerscience_fac_pubs/413; https://digitalcommons.odu.edu/context/computerscience_fac_pubs/article/1418/viewcontent/Zhu_2025_NeuralTopicModelingviaContextualandGraphInformationOCR.pdf |
| DOI: | 10.18653/v1/2025.emnlp-main.670 |
| Διαθεσιμότητα: | https://digitalcommons.odu.edu/computerscience_fac_pubs/413 https://doi.org/10.18653/v1/2025.emnlp-main.670 https://digitalcommons.odu.edu/context/computerscience_fac_pubs/article/1418/viewcontent/Zhu_2025_NeuralTopicModelingviaContextualandGraphInformationOCR.pdf |
| Rights: | © 2025 Association for Computational Linguistics. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International (CC BY 4.0) License . |
| Αριθμός Καταχώρησης: | edsbas.F57780E9 |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.18653/v1/2025.emnlp-main.670 |
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