Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms

• Autorzy: Mishra Akshansh

Identyfikatory

DOI

10.26628/simp.wtr.v96.1158.147-154

• Języki publikacji: EN

Abstrakty

EN

Text summarization is a technique for condensing a big piece of text into a few key elements that give a general impression of the content. When someone requires a quick and precise summary of a large amount of information, it becomes vital. If done manually, summarizing text can be costly and time-consuming. Natural Language Processing (NLP) is the sub-division of Artificial Intelligence that narrows down the gap between technology and human cognition by extracting the relevant information from the pile of data. In the present work, scientific information regarding the Friction Stir Welding of Aluminium alloys was collected from the abstract of scholarly research papers. For extracting the relevant information from these research abstracts four Natural Language Processing based algorithms i.e. Latent Semantic Analysis (LSA), Luhn Algorithm, Lex Rank Algorithm, and KL-Algorithm were used. In order to evaluate the accuracy score of these algorithms, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) was used. The results showed that the Luhn Algorithm resulted in the highest f1-Score of 0.413 in comparison to other algorithms.

Słowa kluczowe

EN

natural language processing; friction stir welding; aluminum alloy; summarization

PL

przetwarzanie języka naturalnego; spawanie tarciowe; stop aluminium; podsumowanie

• Wydawca: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
• Czasopismo: Welding Technology Review
• Rocznik: 2024
• Tom: R. 96
• Strony: 147--154
• Opis fizyczny: Bibliogr. 31 poz., il., tab.

Twórcy

autor

Mishra Akshansh

• Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy

• https://orcid.org/0000-0003-4939-359X

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).