20.500.12556/RUL-183358 Comparative analysis of text similarity algorithms and their practical applications in computer science Primerjalna analiza algoritmov za podrobnost besedil in njihove praktične uporabe v računalništvu In an era defined by vast volumes of digital text, the capacity to compare, interpret, and quantify textual similarity is a cornerstone of modern computational linguistics and natural language processing (NLP). Text similarity algorithms support critical applications in information retrieval, plagiarism detection, sentiment analysis, text summarization, and beyond. This paper provides a comprehensive survey and comparative analysis of established text similarity algorithms, including edit-distance-based metrics (Levenshtein and DamerauLevenshtein), character-based measures (Jaro and Jaro-Winkler), local sequence alignment (Smith-Waterman), vector-based semantic measures (Cosine similarity), and methods reliant on subsequence statistics (N-gram similarity). Each algorithm is analyzed in terms of its underlying theoretical foundations, computational complexity, performance characteristics, and domain-specific suitability. While traditional approaches excel in correcting typographical errors or identifying subtle lexical variations, more robust methods handle semantically rich corpora, larger text bodies, and intricate linguistic phenomena. Moreover, potential avenues for improvement are explored, including hybridization of existing approaches and the integration of emerging machine learning and deep neural models. This holistic examination aims to inform the selection and development of text similarity measures for diverse real-world applications and to guide future research directions in computational linguistics. Primerjava in merjenje podobnosti med digitalnimi besedili sta ključna za računalniško lingvistiko in obdelavo naravnega jezika. Algoritmi za podobnost se uporabljajo pri iskanju informacij, zaznavanju plagiatorstva, analizi sentimenta in povzemanju besedil. Prispevek predstavlja primerjalno analizo uveljavljenih metod, kot so Levenshteinova razdalja, Jaro-Winkler, SmithWaterman, kosinusna podobnost in N-grami. Ocenjene so glede na teoretične osnove, računsko zahtevnost, učinkovitost in primernost za različna področja. Tradicionalne metode so učinkovite pri zaznavanju napak in leksikalnih razlik, naprednejše pa pri obravnavi semantično bogatih in daljših besedil. Raziskane so tudi možnosti izboljšav z združevanjem pristopov in uporabo metod strojnega učenja. Namen analize je usmerjati uporabo in nadaljnji razvoj teh algoritmov. text similarity algorithms natural language processing computational linguistics digitalno besedilo računalniška lingvistika naravni jezik Levenshteinova razdalja strojno učenje true false false Angleški jezik Slovenski jezik Članek v reviji 2026-06-11 15:15:45 2026-06-11 15:15:58 2026-06-12 04:18:45 0000-00-00 00:00:00 2025 0 0 Str. 151-156 št. 3 Letn. 92 2025 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.912:81'322 0013-5852 280478723 RAZ_Poljak_Josip_2025.pdf RAZ_Poljak_Josip_2025.pdf 1 33525AE74D35B4A3E03D1C30E85EF0DB 80a26696583f531a16882537d3b6f29ceca5645fe6e075d8602554af112d2c79 ab6581ab-6597-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=235376 https://ev.fe.uni-lj.si/3-2025/Poljak.pdf 0 c26f79ce-6597-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=235377 Fakulteta za elektrotehniko 0 0 0