<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Subject categorisation experiments with AI in MTMT</dcterms:title><dcterms:identifier>https://hdl.handle.net/21.15109/ARP/VWQFD2</dcterms:identifier><dcterms:creator>Micsik, András</dcterms:creator><dcterms:creator>Tanácsi, Roland</dcterms:creator><dcterms:publisher>ARP</dcterms:publisher><dcterms:issued>2026-05-08</dcterms:issued><dcterms:modified>2026-05-12T12:55:37Z</dcterms:modified><dcterms:description>Code, sample data and results for subject categorisation experiments with AI in MTMT</dcterms:description><dcterms:subject>Computer and Information Science</dcterms:subject><dcterms:subject>subject classification</dcterms:subject><dcterms:subject>scientific categorization</dcterms:subject><dcterms:subject>transformer models</dcterms:subject><dcterms:subject>Support Vector Classifier</dcterms:subject><dcterms:subject>data cleaning</dcterms:subject><dcterms:subject>large language models</dcterms:subject><dcterms:language>English</dcterms:language><dcterms:IsSupplementTo>Tanácsi, R., &amp; Micsik, A. (2026). A Comparative Evaluation of AI Approaches to Large-Scale Scientific Subject Classification. Big Data and Cognitive Computing, 10(5), 151., doi, 10.3390/bdcc10050151, https://doi.org/10.3390/bdcc10050151</dcterms:IsSupplementTo><dcterms:date>2025-11-15</dcterms:date><dcterms:contributor>Micsik, András</dcterms:contributor><dcterms:dateSubmitted>2026-02-03</dcterms:dateSubmitted><dcterms:license>CC BY-NC-ND 4.0</dcterms:license></metadata>