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Sprachliche Steuerung in der Technischen Dokumentation mit Controlled-Language-Checkern und Authoring-Memory-Systemen : Untersuchungen zur Verbesserung der Effizienz von Schreib- und Übersetzungsprozessen
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BLLDB
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UB Frankfurt Linguistik
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| 12 |
Digital Scholarship in the Humanities : Journal of the Alliance of Digital Humanities Organizations
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Institut für Empirische Sprachwissenschaft
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UB Frankfurt Linguistik
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Towards Multipurpose Readability Assessment
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In: Boise State University Theses and Dissertations (2016)
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BASE
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| 17 |
CEST: City Event Summarization using Twitter
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In: Computer Science Graduate Projects and Theses (2016)
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BASE
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Radical Recognition in Off-Line Handwritten Chinese Characters Using Non-Negative Matrix Factorization
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In: Senior Projects Spring 2016 (2016)
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Abstract:
In the past decade, handwritten Chinese character recognition has received renewed interest with the emergence of touch screen devices. Other popular applications include on-line Chinese character dictionary look-up and visual translation in mobile phone applications. Due to the complex structure of Chinese characters, this classification task is not exactly an easy one, as it involves knowledge from mathematics, computer science, and linguistics. Given a large image database of handwritten character data, the goal of my senior project is to use Non-Negative Matrix Factorization (NMF), a recent method for finding a suitable representation (parts-based representation) of image data, to detect specific sub-components in Chinese characters. NMF has only been applied to typed (printed) Chinese characters in different fonts. This project focuses specifically on how well NMF works on handwritten characters. In addition, research in Chinese character classification has mainly been done using holistic approaches - treating each character as an inseparable unit. By using NMF, this project takes a different approach by focusing on a more specific problem in Chinese character classification: radical (sub-component) detection. Finally, a possible application of radical detection will be proposed. This interactive application can potentially help Chinese language learners better recognize characters by radicals.
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Keyword:
Applied Linguistics; Applied Mathematics; Applied Statistics; Artificial Intelligence and Robotics; Chinese Character Recognition; Computational Linguistics; Computer Sciences; Graphics and Human Computer Interfaces; Linguistics; Machine Learning; Non-Negative Matrix Factorization; Optical Character Recognition
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URL: https://digitalcommons.bard.edu/cgi/viewcontent.cgi?article=1176&context=senproj_s2016
https://digitalcommons.bard.edu/senproj_s2016/367
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| 19 |
FORENSIC LINGUISTICS: AUTOMATIC WEB AUTHOR IDENTIFICATION
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VOROBEVA A.A.. - : Федеральное государственное автономное образовательное учреждение высшего образования «Санкт-Петербургский национальный исследовательский университет информационных технологий, механики и оптики», 2016
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