Detecting Semantic Similarity Of Documents Using Natural Language Processing

Agarwala, Saurabh; Anagawadi, Aniketh; Guddeti, Ram Mohana Reddy

doi:10.1016/j.procs.2021.05.076

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…Since ground-truth information is unavailable for every news item, this approach provides a means for assessing news precision. We make use of the similarity approach presented in [37] in order to find comparable news articles that were already evaluated and stored in the database. At first, the considered news is processed, and a bag of words is generated through the combination of several embedding algorithms, such as Word2vec or GloVe.…”

Section: Fact Comparisonmentioning

confidence: 99%

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Marche,

Cabiddu,

Castangia

et al. 2023

IEEE Trans. Serv. Comput.

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Technological development combined with the evolution of the Internet has made it possible to reach an increasing number of people over the years and given them the opportunity to access information published on the network. The growth in the number of fake news generated daily, combined with the simplicity with which it is possible to share them, has created such a large phenomenon that it has become immediately uncontrollable. Furthermore, the quality with which malicious content is made is increasingly high so even professional experts, such as journalists, have difficulty recognizing which news is fake and which is real. This paper aims to implement an architecture that provides a service to final users that assures the reliability of news providers and the quality of news based on innovative tools. The proposed models take advantage of several Machine Learning approaches for fake news detection tasks and take into account well-known attacks on trust. Finally, the implemented architecture is tested with a well-known dataset and shows how the proposed models can effectively identify fake news and isolate malicious sources.

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Section: Fact Comparisonmentioning

confidence: 99%

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Marche,

Cabiddu,

Castangia

et al. 2023

IEEE Trans. Serv. Comput.

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show abstract

“…The approach to combining this method is text mining, which extracts keywords and composes document vectors to see similarities between documents [8]. Cosine similarity can compare documents by measuring the frequency of certain words (vectors) in the document [19]. The linkages between documents are visualized in a graph form, allowing network analysis to be carried out to obtain information about activities on the research map [8].…”

Section: Introductionmentioning

confidence: 99%

Mapping and predicting research trends in international journal publications using graph and topic modeling

Suyanto

Djatna

Wijaya

2023

IJEECS

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Researchers and journal managers need summary information, such as research maps and trends. Topic and words-based document content analysis alternative to science mapping and trend prediction based on bibliographic analysis. The data are a collection of journal articles/proceeding documents and metadata for 2011-2020 published by the International Journal of Electrical and Computer Engineering (IJECE). A combination of several techniques and methods is used, such as text mining, topic modeling, cosine similarity, network analysis, graph theory, and seasonal autoregressive integrated moving average (SARIMA). This research has produced research topics, mapping, trend prediction, and visualization according to its objectives. The results of the topic coherence test obtained the optimal number of topics, as many as 6. The results of the topics were evaluated by experts to be given labels and areas of focus. On the research map for each topic, information is found on trending research, the most popular research, research that is central to the research group, and critical research on the development of the group path. It also identifies the type of breakthrough, incremental, and research gap. Predictions of research trends obtained are based on topics and words that describe the development of research. Visualization is descriptive and predictive.

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Improving knowledge capture and retrieval in the BIM environment: Combining case-based reasoning and natural language processing

Wang

Meng

Zhu

2022

Automation in Construction

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Detecting Semantic Similarity Of Documents Using Natural Language Processing

Cited by 9 publications

References 6 publications

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Mapping and predicting research trends in international journal publications using graph and topic modeling

Improving knowledge capture and retrieval in the BIM environment: Combining case-based reasoning and natural language processing

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