Results 71 to 80 of about 975,825 (180)

Automatic Seed Word Selection for Topic Modeling

IEEE Access
Topic modeling is widely used to uncover latent semantic topics from a corpus. However, topic models often struggle to identify minor topics due to their tendency to prioritize dominant patterns in the data. They are also hindered by polysemous words and
Dahyun Jeong, Jeongin Hwang, Yunjin Choi, Yoon-Yeong Kim   +3 more
doaj   +1 more source

Topic Modeling

Im KONDE-Projekt, das aus Hochschulraumstrukturmitteln finanziert wird, beschäftigten sich sieben universitäre Partner und drei weitere Einrichtungen aus unterschiedlichen Blickwinkeln mit theoretischen und praktischen Aspekten der Digitalen Edition. Ein Outcome des Projektes stellt das Weißbuch dar, welches über 200 Artikel zum Thema Digitale Edition ...
Yanghui Rao, Qing Li
  +5 more sources

A New Approach to Speeding Up Topic Modeling [PDF]

, 2014
Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology.
Jia Zeng, Senior Member, Xiao-qin Cao, Zhi-qiang Liu   +3 more
core  

Research Community Mining via Generalized Topic Modeling [PDF]

Mehran University Research Journal of Engineering and Technology, 2012
Mining research community on the basis of hidden relationships present between its entities is important from academic recommendation point of view. Previous approaches discovered research community by using network connectivity based distance measures
Ali Daud, Muhammad Akram Shaikh, Faqir Muhammad   +2 more
doaj  

Visualizing Topic Uncertainty in Topic Modelling

, 2023
Word clouds became a standard tool for presenting results of natural language processing methods such as topic modelling. They exhibit most important words, where word size is often chosen proportional to the relevance of words within a topic. In the latent Dirichlet allocation (LDA) model, word clouds are graphical presentations of a vector of weights
openaire   +3 more sources

Topic-aware response selection for dialog systems

Natural Language Processing Journal
It is challenging for a persona-based chitchat system to return responses consistent with the dialog context and the persona of the agent. This particularly holds for a retrieval-based chitchat system that selects the most appropriate response from a set
Wei Yuan, Zongyang Ma, Aijun An, Jimmy Xiangji Huang   +3 more
doaj   +1 more source

A Gentle Introduction to Topic Modeling Using Python

Theological Librarianship, 2018
Topic modeling is a data mining method which can be used to understand and categorize large corpora of data; as such, it is a tool which theological librarians can use in their professional workflows and scholarly practices.
Micah D. Saxton
doaj   +1 more source

Kernel Topic Models

, 2011
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions.
Hennig, P., Stern, D., Herbich, R., Graepel, T.   +3 more
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Syntactic Topic Models

, 2010
The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents.
Boyd-Graber, Jordan, Blei, David M.
openaire   +2 more sources

Integration of Neural Embeddings and Probabilistic Models in Topic Modeling

Applied Artificial Intelligence
Topic modeling, a way to find topics in large volumes of text, has grown with the help of deep learning. This paper presents two novel approaches to topic modeling by integrating embeddings derived from Bert-Topic with the multi-grain clustering topic ...
Pantea Koochemeshkian, Nizar Bouguila
doaj   +1 more source