Browsing by Author Nguyen, Le-Minh

Jump to: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
or enter first few letters:  
Showing results 1 to 2 of 2
  • Prev
  • 1
  • Next
  • item.jpg
  • Conference Paper


  • Authors: Vuong, Thi-Hai-Yen; Nguyen, Thi-Thu-Trang; Tran, Nhu-Thuat; Nguyen, Le-Minh (2019)

  • In the field of data management, users traditionally manipulates their data using structured query language (SQL). However, this method requires an understanding of relational database, data schema, and SQL syntax as well as the way it works. Database manipulation using natural language, therefore, is much more convenient since any normal user can interact with their data without a background of database and SQL. This is, however, really tough because transforming natural language commands into SQL queries is a challenging task in natural language processing and understanding. In this paper, we propose a novel two-phase approach to automatically analyzing and converting natural langua...

  • document.pdf.jpg
  • Article


  • Authors: Trieu, Hai-Long; Nguyen, Phuong-Thai; Nguyen, Le-Minh (2015)

  • The sentence alignment approach proposed by Moore, 2002 (M-Align) is an effective method which gets a rela-tively high performance based on mbination of length-based and word correspondences. Nevertheless, despite the high precision, M-Align usually gets a low recall especially when dealing with sparse data problem. We pro-pose an algorithm which not only exploits advantages of M-Align but overcomes the weakness of this baseline method by using a new feature in sentence alignment, word clustering. Experiments shows an mprovement on the baseline method up to 30% recall while precision is reasonable.

Browsing by Author Nguyen, Le-Minh

Jump to: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
or enter first few letters:  
Showing results 1 to 2 of 2
  • item.jpg
  • Conference Paper


  • Authors: Vuong, Thi-Hai-Yen; Nguyen, Thi-Thu-Trang; Tran, Nhu-Thuat; Nguyen, Le-Minh (2019)

  • In the field of data management, users traditionally manipulates their data using structured query language (SQL). However, this method requires an understanding of relational database, data schema, and SQL syntax as well as the way it works. Database manipulation using natural language, therefore, is much more convenient since any normal user can interact with their data without a background of database and SQL. This is, however, really tough because transforming natural language commands into SQL queries is a challenging task in natural language processing and understanding. In this paper, we propose a novel two-phase approach to automatically analyzing and converting natural langua...

  • document.pdf.jpg
  • Article


  • Authors: Trieu, Hai-Long; Nguyen, Phuong-Thai; Nguyen, Le-Minh (2015)

  • The sentence alignment approach proposed by Moore, 2002 (M-Align) is an effective method which gets a rela-tively high performance based on mbination of length-based and word correspondences. Nevertheless, despite the high precision, M-Align usually gets a low recall especially when dealing with sparse data problem. We pro-pose an algorithm which not only exploits advantages of M-Align but overcomes the weakness of this baseline method by using a new feature in sentence alignment, word clustering. Experiments shows an mprovement on the baseline method up to 30% recall while precision is reasonable.