Difference between revisions of "Latent Dirichlet Allocation (LDA)"

From
Jump to: navigation, search
m
m
Line 10: Line 10:
 
* [[Topic Model/Mapping]]
 
* [[Topic Model/Mapping]]
 
* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 
* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 +
* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 +
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
 
* [https://www.crummy.com/software/BeautifulSoup/ Beautiful Soup] a Python library designed for quick turnaround projects like screen-scraping
 
* [https://www.crummy.com/software/BeautifulSoup/ Beautiful Soup] a Python library designed for quick turnaround projects like screen-scraping
 
* [[Term Frequency–Inverse Document Frequency (TF-IDF)]]
 
* [[Term Frequency–Inverse Document Frequency (TF-IDF)]]

Revision as of 14:49, 2 September 2023

Youtube search... ...Google search


In Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's presence is attributable to one of the document's topics. LDA is an example of Topic Model/Mapping.