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Everything about Dynamic topic model
Time-series clustering method
Dynamic topic models are generative models in natural language processing that analyze the evolution of latent topics within a collection of documents over time. This family of topic models was proposed by David Blei and John Lafferty and was initially an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Dynamic topic modelA 5-minute orientation
- Skim a recent paper: Parallel dynamic topic modeling via evolving topic adjustment and term weighting schemeInfluential work since 2022
- Pick up a classic book: Wavelets in NeuroscienceThe most-reprinted book on the subject
- Read the landmark paper: Video Behaviour Mining Using a Dynamic Topic ModelCited 86 times
- Go sideways: Latent Dirichlet allocationA closely related topic
- Go sideways: Dirichlet-multinomial distributionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Video Behaviour Mining Using a Dynamic Topic Model
Tracking urban geo-topics based on dynamic topic model
Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec
Dynamic non-parametric joint sentiment topic mixture model
Dynamic joint sentiment-topic model
Adding community and dynamic to topic models
User clustering in a dynamic social network topic model for short text streams
Forecasting Financial Market Volatility Using a Dynamic Topic Model
🆕 Influential since 2022
Parallel dynamic topic modeling via evolving topic adjustment and term weighting scheme
Topic model for long document extractive summarization with sentence-level features and dynamic memory unit
Joint dynamic topic model for recognition of lead-lag relationship in two text corpora
Mining and evolutionary trends of STEAM research topics based on the dynamic topic model
Neural Dynamic Focused Topic Model
📚 Books worth reading
Wavelets in NeuroscienceAlexander E. Hramov, Alexey A. Koronovskii · 2014
Analysis of Neurophysiological Brain FunctioningChristian Uhl · 1998
Mathematical Modeling of Complex Biological Systems: A Kinetic Theory Approach (Modeling and Simulation in Science, Engineering and Technology)Abdelghani Bellouquid, Marcello Delitala · 2007
Targeted Cancer Treatment In Silico Small Molecule Inhibitors And Oncolytic VirusesDominik Wodarz · 2013
Modelling And Simulation In Fluid Dynamics In Porous MediaGon Alo Pena · 2012- 📕Mathematical methods for analysis of a complex diseaseF. C. Hoppensteadt · 2011
Entropy and the quantum IIArizona School of Analysis with Applications (2nd 2010 University of Arizona) · 2011- 📕Biological fluid dynamicsAnita T. Layton, Sarah D. Olson · 2014
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Latent Dirichlet allocation In natural language processing, latent Dirichlet allocation (LDA) is a generative statistical model that explains how a collection of text documents can…
- Dirichlet-multinomial distribution In probability theory and statistics, the Dirichlet-multinomial distribution is a family of discrete multivariate probability distributions on a finite…
- Hierarchical Dirichlet process In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a…
- Pachinko allocation In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover…
- Topic model In natural language processing, a topic model is a type of probabilistic, neural, or algebraic model for discovering the abstract topics that occur in…
- Categorical distribution In probability theory and statistics, a categorical distribution (also called a generalized Bernoulli distribution, multinoulli distribution) is a discrete…
- Mixture model In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring…
- Probabilistic latent semantic analysis Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles)…