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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2010 / Cilt: 25 - Sayı: 3

Automatic web based language identification and translation system

Web tabanlı otomatik dil tanıma ve çevirme sistemi

Uraz YAVANOĞLU
Şeref SAĞIROĞLU
Pages
483–494
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is expected that this study will help users to use internet more effectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents new methods to identify web contents, containing MS Word and HTML documents indifferent languages and to translate them into specified languages.
📊
Key Findings & Data:Documents belonging to 15 languageswere used in test.
💡
Conclusion & Impact:It is expected that this study will help users to use internet more effectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This study presents new methods to identify web contents, containing MS Word and HTML documents indifferent languages and to translate them into specified languages. The identification problem can be seen as aspecific instance of the more general problem of an item classification though its attributes. This novel approachis based on artificial neural network model to recognize the languages. Documents belonging to 15 languageswere used in test. The results have shown that the approach presented in this work is very successful to meet theexpectations in real-time language identification accuracy and translate into 40 different languages with the helpof a developed platform. It is expected that this study will help users to use internet more effectively.

Özet

Bu çalışmada, internet ortamında bulunan MS Word ve HTML sayısal belgelerinin dilini tanımak ve sunulanbilgilerin içeriğini farklı dillere çevirmek için bir sistem geliştirilmiştir. Dil tanıma sorunu aslında daha genel birproblem olan özniteliklerin sınıflandırılması olarak gözükmektedir. Geliştirilen sistem farklı işlemlerinyapılmasını kolaylaştırmak için kullanıcıların içerik dillerini hiç bilmedikleri internetteki sayısal dokümanlarıniçeriklerinin yapay sinir ağları temelli zeki bir çözüm ile otomatik tespiti ve istenilen 40 dile otomatik olarakçevirebilecekleri sistemden oluşmaktadır. Yapılan testlerde 15 dil için dokümanlar kullanılmıştır. Bu testlerdesistemin gerçek zamanlı olarak çalışma başarısının beklenenin üzerinde olduğu gösterilmiştir. Bu çalışmanın,internet içeriklerinin daha etkin olarak kullanılmasını sağlaması beklenmektedir.

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Cite This Article

YAVANOĞLU, U., SAĞIROĞLU, Ş. (2022). Automatic web based language identification and translation system. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 25 - Sayı: 3, 483–494. https://ajindex.com/articles/522870

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References (14)

  1. Padro M., Padro L., “Comparing Methods for Language Identification” Procesamiento del Lenguaje Natural, Barcelona, 33-35 (2004)
  2. Botha G.R., Zimu V.Z., Barnard E., “Text- based language identification for the South Africanlanguages”, SAIEE Africa Research Journal, Cape Town, 141-146 (2007)
  3. El-Shishiny H., Troussov A., McCloskey DJ., Takeuchi M., Nevidomsky A., Volkov P., “Word Fragments Based Arabic Language Identification”, NEMLAR Conference on Arabic Language Resources and Tools, Mısır, 23-26 (2004)
  4. Kruengkrai C., Srichaivattana P., Sornlertlamvanich V., Isahara H., "Language Identification Based on String Kernels" Communications and Information Technology, Pekin, 896-899 (2005)
  5. Zavarsky P., Wada S., Mikami Y.,”Language and Encoding Scheme Identification of Extremely Large Sets of Multilingual Text Documents”, The 10th Machine Translation Summit, Puket, 354- 355 (2005)
  6. Peng F., Schuurmans D., Wang S.,"Language and Task Independent Text Categorization with Simple Language Models", North American Chapter of the Association for Computational Linguistics - Human Language Technologies, Edmonton, 110-117 (2003)
  7. Nair A.S., Nair V. V., Chandra V. S. S., "Hidden Markov Model Based Identification of Transliterated Regional Language Words in Text Documents", Twentieth International Joint Conference on Artificial Intelligence, Haydarabad, 87-91 (2007)
  8. Ahmed B., Cha S-H.,, Tappert C.,"Language Identification from Text Using N-gram Based Cumulative Frequency Addition", Student/Faculty Research Day, New York, 121- 128 (2004)
  9. Constable P.G., "Toward a Model for Language Identification", Summer Institute of Linguistics International Working Papers”, Dublin (2002)
  10. Adams G., Resnik P., "A Language Identification Application Built on the Java Client/Server Platform", The European Chapter of the Association of Computaional Linguistics Workshop, İspanya (1997)
  11. Ölveck T., "N-Gram based Statistics Aimed. at Language Identification", Student Research Conference in Informatics and Information Technologies, Brastilava, 1-7 (2005)
  12. Bilcu, E.B., Astola J., “A Hybrid Neural Network for Language Identification from Text”, Machine. Learning for Signal Processing Conference, Maynooth, 253-258 (2006)
  13. Liu Y-H., Chang F., Lin C-C., "Language Identification of Character Images Using Machine Learning Techniques", International Conference on Document Analysis and Recognition, Seul, 630-634 (2005)
  14. Zhu G., Yu X.,Li Y., Doermann D., "Unco