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 …
Automatic text summarization: A comprehensive survey
Abstract Automatic Text Summarization (ATS) is becoming much more important because of
the huge amount of textual content that grows exponentially on the Internet and the various …
the huge amount of textual content that grows exponentially on the Internet and the various …
Beyond semantic distance: Automated scoring of divergent thinking greatly improves with large language models
Automated scoring for divergent thinking (DT) seeks to overcome a key obstacle to creativity
measurement: the effort, cost, and reliability of scoring open-ended tests. For a common test …
measurement: the effort, cost, and reliability of scoring open-ended tests. For a common test …
Graph neural networks for natural language processing: A survey
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Machine learning and AI in marketing–Connecting computing power to human insights
Artificial intelligence (AI) agents driven by machine learning algorithms are rapidly
transforming the business world, generating heightened interest from researchers. In this …
transforming the business world, generating heightened interest from researchers. In this …
The evolution of topic modeling
Topic models have been applied to everything from books to newspapers to social media
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …
Top2vec: Distributed representations of topics
D Angelov - arxiv preprint arxiv:2008.09470, 2020 - arxiv.org
Topic modeling is used for discovering latent semantic structure, usually referred to as
topics, in a large collection of documents. The most widely used methods are Latent Dirichlet …
topics, in a large collection of documents. The most widely used methods are Latent Dirichlet …
A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …
groups so that similar samples belong to the same cluster while dissimilar samples belong …
A review of topic modeling methods
Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic
modeling have been developed which consider many kinds of relationships and restrictions …
modeling have been developed which consider many kinds of relationships and restrictions …
A comprehensive survey and analysis of generative models in machine learning
Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …
learning, we come across many scenarios when directly learning a target is intractable …