An Identification Method of News Scientific Intelligence Based on TF-IDF

Pan, Lu; Tang, Haibo; Zhou, Lei; Wang, Liuyang; Zhu, Qing

doi:10.1109/dcabes.2015.131

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…In order to improve the value and accuracy of science information that is pushed in this paper, an intelligence dichotomous method for science information categorization to identify science information from massive Web news is presents. During the experiment, 85.3% recognition rate of the recognition non-tech news are realized and 82.9% accuracy rate, the results show that the method can effectively identify Web science information news and reduce the amount of independent news [4].…”

Section: Related Workmentioning

confidence: 90%

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Text Document Classification by using WordNet Ontology and Neural Network

Gawade¹,

Mane²,

Ghone³

et al. 2018

IJCA

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Every day the mass of information available, merely finding the relevant information is not the only task of automatic text classification systems. The main problem is to classify which documents are relevant and which are irrelevant. The Automated text classification consists of automatically organizing clustered data. We propose a method of automatic text classification using Convolutional Neural Network based on the disambiguation of the meaning of the word we use the WordNet ontology and word embedding algorithm to eliminate the ambiguity of words so that each word is replaced by its meaning in suitable context. The closest ancestors of the senses of all the words in a given document are selected as folders for the specified document.

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Section: Related Workmentioning

confidence: 90%

“…Step 3: We define an elimination factor TF/IDF for each word as = Number of occurrence in its own context / Total number of occurrences in all contexts [4].…”

Section: Fig 3: Proposed Architecturementioning

confidence: 99%

Text Document Classification by using WordNet Ontology and Neural Network

Gawade¹,

Mane²,

Ghone³

et al. 2018

IJCA

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show abstract

“…Text Categorization [5], whose core is to build a function from a single text to category, is an important technology of data processing [6], divided into supervised learning unsupervised, semi-supervised learning, enhance learning and learning [7]. This algorithm for title classification of scientific news is an algorithm of Chinese text categorization [8] and bases on title of scientific news.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Title Classification on Scientific News

Shao¹,

Zhu²,

Yan³

et al. 2016

Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications

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Nowadays, with the rapid development of Internet, news is growing explosively. In order to improve the practical value of scientific news, an algorithm of text categorization which is used to classify scientific news is designed. The number of scientific news is 16034, which is gotten from web pages. During the experiment, scientific news of life, medical scientific news, scientific news of earth, mathematical and physical scientific news, chemical scientific news and informational scientific news respectively achieved 72.04%, 64.05%, 71.59%, 67.88%, 66.84% and 61.35% accuracy rate. This algorithm gets good effect and improves the value of the scientific news collected from Web and the accuracy of scientific news detailed classification.

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Discovering New Sensitive Words Based on Sensitive Information Categorization

Liu

Cai

et al. 2019

Lecture Notes in Computer Science

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An Identification Method of News Scientific Intelligence Based on TF-IDF

Cited by 4 publications

References 21 publications

Text Document Classification by using WordNet Ontology and Neural Network

Text Document Classification by using WordNet Ontology and Neural Network

An Algorithm for Title Classification on Scientific News

Discovering New Sensitive Words Based on Sensitive Information Categorization

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