Topic modeling algorithms and applications: A survey
Topic modeling is used in information retrieval to infer the hidden themes in a collection of
documents and thus provides an automatic means to organize, understand and summarize …
documents and thus provides an automatic means to organize, understand and summarize …
A systematic review of the use of topic models for short text social media analysis
Recently, research on short text topic models has addressed the challenges of social media
datasets. These models are typically evaluated using automated measures. However, recent …
datasets. These models are typically evaluated using automated measures. However, recent …
BERTopic: Neural topic modeling with a class-based TF-IDF procedure
M Grootendorst - arxiv preprint arxiv:2203.05794, 2022 - arxiv.org
Topic models can be useful tools to discover latent topics in collections of documents.
Recent studies have shown the feasibility of approach topic modeling as a clustering task …
Recent studies have shown the feasibility of approach topic modeling as a clustering task …
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 …
Is neural topic modelling better than clustering? An empirical study on clustering with contextual embeddings for topics
Recent work incorporates pre-trained word embeddings such as BERT embeddings into
Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality …
Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality …
Effective neural topic modeling with embedding clustering regularization
Topic models have been prevalent for decades with various applications. However, existing
topic models commonly suffer from the notorious topic collapsing: discovered topics …
topic models commonly suffer from the notorious topic collapsing: discovered topics …
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 …
Topic modeling revisited: A document graph-based neural network perspective
Most topic modeling approaches are based on the bag-of-words assumption, where each
word is required to be conditionally independent in the same document. As a result, both of …
word is required to be conditionally independent in the same document. As a result, both of …
Neural topic model via optimal transport
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained
increasingly research interest due to their promising results on text analysis. However, it is …
increasingly research interest due to their promising results on text analysis. However, it is …
Sawtooth factorial topic embeddings guided gamma belief network
Hierarchical topic models such as the gamma belief network (GBN) have delivered
promising results in mining multi-layer document representations and discovering …
promising results in mining multi-layer document representations and discovering …