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Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Computers, Materials & Continua 2024年第5期79卷 2209-2228页

作者： Halil ibrahim okur Kadir Tohma Ahmet Sertbas Department of Computer Engineering Faculty of Engineering and Natural SciencesIskenderun Technical UniversityHatay31200Turkey Department of Computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34310Turkey

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing *** study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)*** on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from *** adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational *** conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural *** results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP *** of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational *** demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)***,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

关键词： Text classification relation extraction NER distant supervision deep learning machine learning

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MgO and ZnO Composite Thin Films Using the Spin Coating Method on Microscope Glasses

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Materials Sciences and Applications 2015年第1期6卷 40-47页

作者： Ayse Kaya Balta Ozlem Ertek Nail Eker ibrahim okur Department of Physics Sakarya UniversitySakaryaTurkey

In this study, a new route to produce pure and composite ZnO-MgO thin films has been presented. In the process the pure ZnO thin films were the starting point, ending up with MgO by doping various percentages (from 0% to 100%) of Mg with the help of sol-gel spin coating technique. The crystal phases in all doping levels have been obtained when the samples annealed at 600℃ for a duration of 6 hours. The X-ray diffraction (XRD) spectra, the scanning electron microscopy (SEM) micrographs and UV-Vis absorption spectra have been performed to elucidate the composed film structures.

关键词： ZnO MgO Doping Microscope Glass Spin Coating Thin Film

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