Topic Model/Mapping - Revision history

BPeat at 13:59, 16 September 2023

2023-09-16T13:59:59Z

BPeat at 13:58, 16 September 2023

2023-09-16T13:58:50Z

BPeat at 13:53, 16 September 2023

2023-09-16T13:53:26Z

BPeat at 13:52, 16 September 2023

2023-09-16T13:52:13Z

BPeat at 18:43, 2 February 2019

2019-02-02T18:43:26Z

BPeat at 03:47, 8 January 2019

2019-01-08T03:47:07Z

BPeat at 03:44, 8 January 2019

2019-01-08T03:44:14Z

BPeat at 03:00, 8 January 2019

2019-01-08T03:00:08Z

BPeat: Created page with "[http://www.youtube.com/results?search_query=topic+model+mapping+nlp+natural+language+semantics Youtube search...] [http://www.google.com/search?q=topic+model+mapping+nlp+natu..."

2019-01-08T02:57:51Z

Created page with "[http://www.youtube.com/results?search_query=topic+model+mapping+nlp+natural+language+semantics Youtube search...] [http://www.google.com/search?q=topic+model+mapping+nlp+natu..."

New page

[http://www.youtube.com/results?search_query=topic+model+mapping+nlp+natural+language+semantics Youtube search...]
[http://www.google.com/search?q=topic+model+mapping+nlp+natural+language+semantics+machine+learning+ML ...Google search]

* [[Natural Language Processing (NLP)]]
* [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)]]
* [[Probabilistic Latent Semantic Analysis (PLSA)]]
* [[Latent Dirichlet Allocation (LDA)]]

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]].

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 * [[Latent Dirichlet Allocation (LDA)]]
-* [[Natural Language Processing (NLP)]]
+* [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation (NLG)]] ... [[Natural Language Classification (NLC)|Classification (NLC)]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding (NLU)]] ... [[Language Translation|Translation]] ... [[Summarization]] ... [[Sentiment Analysis|Sentiment]] ... [[Natural Language Tools & Services|Tools]]
 * [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>Discovering Topics</b>: The main goal of topic modeling is to discover and define the [[latent]] topics that best explain the structure of the document collection. By analyzing the inferred [[latent]] variables, you can identify the topics and understand which words are associated with each topic.
-* <b>Applications</b>: Once the [[latent]] topics are uncovered, they can be used for various [[Natural Language Processing (NLP)|NLP]] tasks, such as document clustering, document classification, content recommendation, and summarization. These [[latent]] topics provide a structured representation of the content within the corpus.
+* <b>Applications</b>: Once the [[latent]] topics are uncovered, they can be used for various [[Natural Language Processing (NLP)|NLP]] tasks, such as [[Clustering|document clustering]], [[Natural Language Classification (NLC)|document classification]], [Recommendation|content recommendation]], and [[summarization]]. These [[latent]] topics provide a structured representation of the content within the corpus.

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 = Latent Variables Role Topic Modeling =
-[[Latent]] variables play a central role in topic modeling, which is a technique used in natural language processing (NLP) and text analysis to uncover the hidden thematic structure within a collection of documents. The [[latent]] variables in topic modeling represent the topics or themes that are not explicitly observed but are inferred from the patterns in the text data. Here's how [[latent]] variables are related to topic modeling:
+[[Latent]] variables play a central role in topic modeling, which is a technique used in [[Natural Language Processing (NLP)]] and text analysis to uncover the hidden thematic structure within a collection of documents. The [[latent]] variables in topic modeling represent the topics or themes that are not explicitly observed but are inferred from the patterns in the text data. Here's how [[latent]] variables are related to topic modeling:
 * <b>Latent Topics</b>: In topic modeling, the fundamental idea is that each document in a corpus is a mixture of [[latent]] topics. These topics represent themes or concepts that are prevalent in the documents but are not directly observed. Each document is characterized by the distribution of these [[latent]] topics.
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 * <b>Discovering Topics</b>: The main goal of topic modeling is to discover and define the [[latent]] topics that best explain the structure of the document collection. By analyzing the inferred [[latent]] variables, you can identify the topics and understand which words are associated with each topic.
-* <b>Applications</b>: Once the [[latent]] topics are uncovered, they can be used for various NLP tasks, such as document clustering, document classification, content recommendation, and summarization. These [[latent]] topics provide a structured representation of the content within the corpus.
+* <b>Applications</b>: Once the [[latent]] topics are uncovered, they can be used for various [[Natural Language Processing (NLP)|NLP]] tasks, such as document clustering, document classification, content recommendation, and summarization. These [[latent]] topics provide a structured representation of the content within the corpus.

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 * associations, representing hypergraph relationships between topics, and
 * occurrences, representing information resources relevant to a particular topic.
 Topic maps are similar to concept maps and mind maps in many respects, though only topic maps are ISO standards. Topic maps are a form of semantic web technology similar to RDF.
 http://upload.wikimedia.org/wikipedia/commons/f/f1/TopicMapKeyConcepts2.PNG

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 * [http://en.wikipedia.org/wiki/Latent_semantic_analysis Latent Semantic Analysis | Wikipedia]
 * [http://en.wikipedia.org/wiki/Explicit_semantic_analysis Explicit semantic analysis | Wikipedia]
+* [http://medium.com/nanonets/topic-modeling-with-lsa-psla-lda-and-lda2vec-555ff65b0b05 Topic Modeling with LSA, PLSA, LDA & lda2Vec | Joyce Xu]
 Topic modelling can be described as a method for finding a group of words (i.e topic) from a collection of documents that best represents the information in the collection. It can also be thought of as a form of text mining – a way to obtain recurring patterns of words in textual material.
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 In machine learning and [[Natural Language Processing (NLP)]], a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear equally in both.
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 [http://www.youtube.com/results?search_query=topic+model+mapping+nlp+natural+language+semantics Youtube search...]
 [http://www.youtube.com/results?search_query=%22topic+mapping%22+%22topic+model%22+machine+learning+nlp+natural+language+semantics+machine+learning+ML ...Google search]
 * [[Latent Dirichlet Allocation (LDA)]]