Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling

Chen, HeGang; Mao, Pengbo; Lu, Yuyin; Rao, Yanghui

doi:10.18653/v1/2023.acl-long.578

Cited by 3 publications

(1 citation statement)

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“…This could be achieved by training a decoder for summarization, generating a summarization for each topic, and subsequently concatenating them. This framework can also be extended to hierarchical topic modeling (Chen et al, 2023;Shahid et al, 2023;Eshima and Mochihashi, 2023), mitigate data sparsity for short text topic modeling (Wu et al, 2022), generate topic-relevant and coherent long texts (Yang et al, 2022), and construct a network of topics together with meaningful relationships between them (Byrne et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport

Xu¹,

Jiang²,

Rao³

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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Language model based methods are powerful techniques for text classification. However, the models have several shortcomings. (1) It is difficult to integrate human knowledge such as keywords. (2) It needs a lot of resources to train the models.(3) It relied on large text data to pretrain. In this paper, we propose Semi-Supervised vMF Neural Topic Modeling (S2vNTM) to overcome these difficulties. S2vNTM takes a few seed keywords as input for topics. S2vNTM leverages the pattern of keywords to identify potential topics, as well as optimize the quality of topics' keywords sets. Across a variety of datasets, S2vNTM outperforms existing semi-supervised topic modeling methods in classification accuracy with limited keywords provided. S2vNTM is at least twice as fast as baselines.

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Section: Discussionmentioning

confidence: 99%

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport

Xu¹,

Jiang²,

Rao³

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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A survey on neural topic models: methods, applications, and challenges

Wu,

Nguyen,

Luu

2024

Artif Intell Rev

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Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the rise of neural networks has facilitated the emergence of a new research field—neural topic models (NTMs). Different from conventional topic models, NTMs directly optimize parameters without requiring model-specific derivations. This endows NTMs with better scalability and flexibility, resulting in significant research attention and plentiful new methods and applications. In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges. Specifically, we systematically organize current NTM methods according to their network structures and introduce the NTMs for various scenarios like short texts and cross-lingual documents. We also discuss a wide range of popular applications built on NTMs. Finally, we highlight the challenges confronted by NTMs to inspire future research.

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Syntax-based argument correlation-enhanced end-to-end model for scientific relation extraction

Zhu,

Gao,

et al. 2024

Neurocomputing

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Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling

Cited by 3 publications

References 0 publications

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport

A survey on neural topic models: methods, applications, and challenges

Syntax-based argument correlation-enhanced end-to-end model for scientific relation extraction

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