Improving Arabic cognitive distortion classification in Twitter using BERTopic
Social media platforms allow users to share thoughts, experiences, and beliefs. These
platforms represent a rich resource for natural language processing techniques to make …
platforms represent a rich resource for natural language processing techniques to make …
[HTML][HTML] An integrated clustering and BERT framework for improved topic modeling
L George, P Sumathy - International Journal of Information Technology, 2023 - Springer
Topic modelling is a machine learning technique that is extensively used in Natural
Language Processing (NLP) applications to infer topics within unstructured textual data …
Language Processing (NLP) applications to infer topics within unstructured textual data …
Wangiri fraud: Pattern analysis and machine-learning-based detection
The rapid growth of the telecommunication landscape leads to a rapid rise of frauds in such
networks. In this article, Wangiri fraud in which users are deceived by being charged for …
networks. In this article, Wangiri fraud in which users are deceived by being charged for …
[PDF][PDF] Identification effect of least square fitting method in archives management
C Ding, H Liang, N Lin, Z Xiong, Z Li, P Xu - Heliyon, 2023 - cell.com
Archives management plays an important role in the current information age. Solving the
problem of identifying and classifying archives is essential for promoting the development of …
problem of identifying and classifying archives is essential for promoting the development of …
A weak-region enhanced Bayesian classification for spam content-based filtering
This article proposes an improved Bayesian scheme by focusing on the region in which
Bayesian may fail to correctly identify labels and improve classification performance by …
Bayesian may fail to correctly identify labels and improve classification performance by …
Neural topic model training with the REBAR gradient estimator
Topic modelling is an important approach of unsupervised machine learning that allows
automatically extracting the main “topics” from large collections of documents. In addition …
automatically extracting the main “topics” from large collections of documents. In addition …
Towards better understanding with uniformity and explicit regularization of embeddings in embedding-based neural topic models
Embedding-based neural topic models could explicitly represent words and topics by
embedding them to a homogeneous feature space, which shows higher interpretability …
embedding them to a homogeneous feature space, which shows higher interpretability …
Augmenting Document Classification Accuracy Through the Integration of Deep Contextual Embeddings.
RK Paladugu, GR Kancherla - Ingénierie des Systèmes d' …, 2024 - search.ebscohost.com
Document classification, a fundamental process within the field of natural language
processing, has benefitted from the recent advancements in deep learning, particularly in …
processing, has benefitted from the recent advancements in deep learning, particularly in …
Analysis of News Article Various Countries on a Specific Event Using Semantic Network Analysis
H Park, J Pak, Y Kim - International Conference on Information Technology …, 2023 - Springer
People today obtain news and information from many sources. This has been made
possible by the exponential growth of the number of news outlets due to the development in …
possible by the exponential growth of the number of news outlets due to the development in …
Neural Topic Modelling with Deep Generative Models
A Kumar - 2023 - opus.lib.uts.edu.au
Topic modelling is a popular task of natural language processing (NLP) aimed to
automatically discover the main, shared topics of a given collection of documents. In …
automatically discover the main, shared topics of a given collection of documents. In …