Probabilistic Latent Semantic Analysis (PLSA) - Revision history

BPeat at 14:07, 16 September 2023

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BPeat at 18:12, 3 February 2019

2019-02-03T18:12:36Z

BPeat at 12:04, 5 January 2019

2019-01-05T12:04:58Z

BPeat: Created page with "[http://www.youtube.com/results?search_query=LSA+PLSA+Probabilistic+Latent+Semantic+Analysis+nlp+nli+natural+language+semantics Youtube search...] * Natural Language Proces..."

2018-09-07T03:09:13Z

Created page with "[http://www.youtube.com/results?search_query=LSA+PLSA+Probabilistic+Latent+Semantic+Analysis+nlp+nli+natural+language+semantics Youtube search...] * Natural Language Proces..."

New page

[http://www.youtube.com/results?search_query=LSA+PLSA+Probabilistic+Latent+Semantic+Analysis+nlp+nli+natural+language+semantics Youtube search...]

* [[Natural Language Processing (NLP), Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)]]
* [http://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)]]

<youtube>BJ0MnawUpaU</youtube>
<youtube>vtadpVDr1hM</youtube>

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 * [[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]]
 * [[Topic Model/Mapping]]
 * [http://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)]]

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 * </b>Applications</b>: PLSA is mainly used for discovering topics in large document collections. By analyzing the word-topic and topic-document distributions learned by PLSA, you can identify the prevalent themes or topics within the corpus.
 = Latent Semantic Analysis (LSA) =
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 * </b>How LSA Works</b>: LSA operates by performing Singular Value Decomposition (SVD) on a term-document matrix. This matrix represents the frequency of terms (words) in documents. SVD reduces the dimensionality of this matrix and extracts [[latent]] semantic patterns. The resulting lower-dimensional representations can help identify relationships between words and documents.
 * </b>Applications</b>: LSA can be used for clustering similar documents, finding related documents in information retrieval, and generating document summaries by identifying key terms and phrases.
 = Key Differences: LSA & PLSA =
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 * LSA does not involve a probabilistic generative process, while PLSA explicitly models the probability of word generation from topics.
 * In PLSA, the number of topics is typically a parameter to be determined, whereas LSA does not inherently model topics.

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 * [[Term Frequency–Inverse Document Frequency (TF-IDF)]]
-PLSA is a probabilistic generative model used for topic modeling in text data. It is an extension of Latent Semantic Analysis that introduces a probabilistic framework to the topic modeling problem.
+PLSA is a probabilistic generative model used for topic modeling in text data. It is an extension of [[Latent]] Semantic Analysis that introduces a probabilistic framework to the topic modeling problem.
-* </b>How PLSA Works</b>: In PLSA, it is assumed that documents are generated through a probabilistic process. Specifically, it assumes that there are latent topics, and each document is a mixture of these topics. Each word in a document is generated from one of these topics with a certain probability.
+* </b>How PLSA Works</b>: In PLSA, it is assumed that documents are generated through a probabilistic process. Specifically, it assumes that there are [[latent]] topics, and each document is a mixture of these topics. Each word in a document is generated from one of these topics with a certain probability.
 * </b>Applications</b>: PLSA is mainly used for discovering topics in large document collections. By analyzing the word-topic and topic-document distributions learned by PLSA, you can identify the prevalent themes or topics within the corpus.
@@ Line 23: / Line 23: @@
 LSA is a technique used for dimensionality reduction and discovering the underlying structure in a collection of documents. It's primarily used for tasks like document clustering, information retrieval, and document summarization.
-* </b>How LSA Works</b>: LSA operates by performing Singular Value Decomposition (SVD) on a term-document matrix. This matrix represents the frequency of terms (words) in documents. SVD reduces the dimensionality of this matrix and extracts latent semantic patterns. The resulting lower-dimensional representations can help identify relationships between words and documents.
+* </b>How LSA Works</b>: LSA operates by performing Singular Value Decomposition (SVD) on a term-document matrix. This matrix represents the frequency of terms (words) in documents. SVD reduces the dimensionality of this matrix and extracts [[latent]] semantic patterns. The resulting lower-dimensional representations can help identify relationships between words and documents.
 * </b>Applications</b>: LSA can be used for clustering similar documents, finding related documents in information retrieval, and generating document summaries by identifying key terms and phrases.

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 [http://www.google.com/search?q=LSA+PLSA+Probabilistic+Latent+Semantic+Analysis+nlp+nli+natural+language+semantics+machine+learning+ML+artificial+intelligence ...Google search]
-* [[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]]
 * [http://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)]]

← Older revision		Revision as of 18:12, 3 February 2019
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	[http://www.youtube.com/results?search_query=LSA+PLSA+Probabilistic+Latent+Semantic+Analysis+nlp+nli+natural+language+semantics Youtube search...]		[http://www.youtube.com/results?search_query=LSA+PLSA+Probabilistic+Latent+Semantic+Analysis+nlp+nli+natural+language+semantics Youtube search...]
		+	[http://www.google.com/search?q=LSA+PLSA+Probabilistic+Latent+Semantic+Analysis+nlp+nli+natural+language+semantics+machine+learning+ML+artificial+intelligence ...Google search]

	* [[Natural Language Processing (NLP)]]		* [[Natural Language Processing (NLP)]]

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 * [http://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)]]
 <youtube>BJ0MnawUpaU</youtube>
 <youtube>vtadpVDr1hM</youtube>