A Survey on Neural Topic Models: Methods, Applications, and Challenges

Wu, Xiaobao; Nguyen, T. Q.; Luu, Anh Tuan

doi:10.21203/rs.3.rs-3049182/v1

Cited by 3 publications

(2 citation statements)

References 99 publications

(145 reference statements)

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“…Each topic (word) embedding represents its semantics. To model latent topics at level ℓ, we calculate its topic-word distribution matrix β (ℓ) following Wu et al (2023b) as…”

Section: Parameterizing Hierarchical Latent Topicsmentioning

confidence: 99%

On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling

Wu,

Pan,

Nguyen

et al. 2024

AAAI

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Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity. However, existing work struggles with producing topic hierarchies of low affinity, rationality, and diversity, which hampers document understanding. To overcome these challenges, we in this paper propose Transport Plan and Context-aware Hierarchical Topic Model (TraCo). Instead of early simple topic dependencies, we propose a transport plan dependency method. It constrains dependencies to ensure their sparsity and balance, and also regularizes topic hierarchy building with them. This improves affinity and diversity of hierarchies. We further propose a context-aware disentangled decoder. Rather than previously entangled decoding, it distributes different semantic granularity to topics at different levels by disentangled decoding. This facilitates the rationality of hierarchies. Experiments on benchmark datasets demonstrate that our method surpasses state-of-the-art baselines, effectively improving the affinity, rationality, and diversity of hierarchical topic modeling with better performance on downstream tasks.

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“…Each topic (word) embedding represents its semantics. To model latent topics at level ℓ, we calculate its topic-word distribution matrix β (ℓ) following Wu et al (2023b) as…”

Section: Parameterizing Hierarchical Latent Topicsmentioning

confidence: 99%

On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling

Wu,

Pan,

Nguyen

et al. 2024

AAAI

View full text Add to dashboard Cite

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“…NTMs [30] are a kind of topic modeling technique that includes various neural network structures. One of the basic NTM model structures is mentioned in [31] (in the subsequent text, the basic NTM model is denoted as NTM).…”

Section: Neural Topic Modelingmentioning

confidence: 99%

MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork Recommendation

Wang,

Maeda,

Kawagoe

2024

Electronics

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Recommender systems help users obtain the content they need from massive amounts of information. Artwork recommender systems is a topic that has attracted attention. However, existing art recommender systems rarely consider user preferences and multimodal information at the same time, while utilizing all the information has the potential to help make better personalized recommendations. To better apply recommender systems to the artwork-recommendation scenario, we propose a new neural topic modeling (NTM)-based multimodal artwork recommender system (MultArtRec), that can take all the information into account at the same time and extract effective features representing user preferences from multimodal content. Also, to improve MultArtRec’s performance on monomodal feature extraction, we add a novel topic loss term to the conventional NTM loss. The first two experiments in this study compare the performances of different models with different monomodal inputs. The results show that MultArtRec can improve the performance with image modality inputs by up to 174.8% compared to the second-best model and improve the performance with text modality inputs by up to 10.7% compared to the second-best model. The third experiment is conducted to compare the performance of MultArtRec with monomodal inputs and multimodal inputs. The results show that the performance of MultArtRec with multimodal inputs can be improved by up to 15.9% compared to monomodal inputs. The last experiment preliminarily tests the versatility of MultArtRec on a fashion recommendation scenario that considers clothing image content and user preferences. The results show that MultArtRec outperforms the other methods across all the metrics.

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Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network

Austin,

Makwana,

Trabelsi

et al. 2024

Data Sci. Eng.

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Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should accurately identify flat and hierarchical topics, especially useful in disciplines where topics can be logically arranged into a tree format. In this paper, we propose Community Topic, a novel algorithm that exploits word co-occurrence networks to mine communities and produces topics. We also evaluate the proposed approach using several metrics and compare it with usual baselines, confirming its good performances. Community Topic enables quick identification of flat topics and topic hierarchy, facilitating the on-demand exploration of sub- and super-topics. It also obtains good results on datasets in different languages.

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A Survey on Neural Topic Models: Methods, Applications, and Challenges

Cited by 3 publications

References 99 publications

On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling

On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling

MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork Recommendation

Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network

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