Text Classification in the Domain of Applied Linguistics as Part of a Pre-editing Module for Machine Translation Systems

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• 关键词：Machine translation (MT) ; Automatic pre ; editing ; Domain adaptation ; Document classification ; TF ; IDF term weighting ; Vector space model ; Cosine similarity
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9811
• 期：1
• 页码：691-698
• 全文大小：947 KB
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• 作者单位：Ksenia Oskina (16)
  
  16. Institute of Applied and Mathematical Linguistics, Moscow State Linguistic University, Moscow, Russia
  
• 丛书名：Speech and Computer
• ISBN：978-3-319-43958-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9811

文摘

This article describes the method of document classification on the basis of a vector space model with regard to the domain of Applied Linguistics for Russian. This method makes it possible to classify input text data in two different categories: applied linguistics texts (AL) and non-applied linguistics texts (nonAL). The proposed method is implemented using the statistical measure of TF-IDF and the evaluation measure of cosine similarity. The study gives promising results and opens up further prospects for the application of this approach to text classification in other languages.