A survey on neural topic models: methods, applications, and challenges
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 …
proportions of documents in an unsupervised fashion. They have been widely used in …
Pre-training is a hot topic: Contextualized document embeddings improve topic coherence
Topic models extract groups of words from documents, whose interpretation as a topic
hopefully allows for a better understanding of the data. However, the resulting word groups …
hopefully allows for a better understanding of the data. However, the resulting word groups …
Infoctm: A mutual information maximization perspective of cross-lingual topic modeling
Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing
aligned latent topics. However, most existing methods suffer from producing repetitive topics …
aligned latent topics. However, most existing methods suffer from producing repetitive topics …
Are neural topic models broken?
Recently, the relationship between automated and human evaluation of topic models has
been called into question. Method developers have staked the efficacy of new topic model …
been called into question. Method developers have staked the efficacy of new topic model …
Efficient topic identification for urgent MOOC Forum posts using BERTopic and traditional topic modeling techniques
N Khodeir, F Elghannam - Education and Information Technologies, 2024 - Springer
MOOC platforms provide a means of communication through forums, allowing learners to
express their difficulties and challenges while studying various courses. Within these forums …
express their difficulties and challenges while studying various courses. Within these forums …
Multilingual and multimodal topic modelling with pretrained embeddings
E Zosa, L Pivovarova - arXiv preprint arXiv:2211.08057, 2022 - arxiv.org
This paper presents M3L-Contrast--a novel multimodal multilingual (M3L) neural topic
model for comparable data that maps texts from multiple languages and images into a …
model for comparable data that maps texts from multiple languages and images into a …
SAE-NTM: Sentence-aware encoder for neural topic modeling
Incorporating external knowledge, such as pre-trained language models (PLMs), into neural
topic modeling has achieved great success in recent years. However, employing PLMs for …
topic modeling has achieved great success in recent years. However, employing PLMs for …
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
Most existing topic models rely on bag-of-words (BOW) representation, which limits their
ability to capture word order information and leads to challenges with out-of-vocabulary …
ability to capture word order information and leads to challenges with out-of-vocabulary …
The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling
In the era of big data and ubiquitous internet connectivity, user feedback data plays a crucial
role in product development and improvement. However, extracting valuable insights from …
role in product development and improvement. However, extracting valuable insights from …
Prompt-optimized self-supervised double-tower contextualized topic model
D Wu, L Yang, W Ma - Multimedia Tools and Applications, 2024 - Springer
Microblog has a large amount of data and is updated quickly with current events.
Unsupervised machine learning models are used for the topic clustering. Complex …
Unsupervised machine learning models are used for the topic clustering. Complex …