English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Topic models are one of the compelling methods for discovering latentsemantics in a document collection. However, it assumes that a document hassufficient co-occurrence information to be effective. However, in short texts,co-occurrence information is minimal, which results in feature sparsity indocument representation. Therefore, existing topic models (probabilistic orneural) mostly fail to mine patterns from them to generate coherent topics. Inthis paper, we take a new approach to short-text topic modeling to address thedata-sparsity issue by extending short text into longer sequences usingexisting pre-trained language models (PLMs). Besides, we provide a simplesolution extending a neural topic model to reduce the effect of noisyout-of-topics text generation from PLMs. We observe that our model cansubstantially improve the performance of short-text topic modeling. Extensiveexperiments on multiple real-world datasets under extreme data sparsityscenarios show that our models can generate high-quality topics outperformingstate-of-the-art models.

Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling