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Subject extraction and keyword extraction from text

Zerui Song

Subject extraction and keyword extraction from text.

Rel. Luciano Lavagno, Gianpiero Cabodi. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021

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Abstract:

LDA is an unsupervised learning topic probability generation model. The input is the document collection and the number of topics, and the output is the topic presented in the form of probability distribution. It is often used for topic modeling, text classification, and opinions. Mining and other fields. It assumes a premise: the document is equivalent to a bag-of-words, the words in the bag are independent and interchangeable, without grammatical structure and order. The basic idea is: each document (Document) is composed of multiple topics (Topic), and each topic has multiple corresponding words (Word) to describe.

Relators: Luciano Lavagno, Gianpiero Cabodi
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 63
Subjects:
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/21280
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