Difference between revisions of "Latent Dirichlet Allocation (LDA)"
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* [[Topic Model/Mapping]] | * [[Topic Model/Mapping]] | ||
| − | * [[Natural Language Processing (NLP)]] | + | * [[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]] |
* [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 14:29, 28 April 2023
Youtube search... ...Google search
- Topic Model/Mapping
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Beautiful Soup a Python library designed for quick turnaround projects like screen-scraping
- Term Frequency–Inverse Document Frequency (TF-IDF)
- Probabilistic Latent Semantic Analysis (PLSA)
- Generative AI ... Conversational AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's Bing ... You ...Google's Bard ... Baidu's Ernie
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.
