scholarly journals Text Document Categorization using Enhanced Sentence Vector Space Model and Bi-Gram Text Representation Model Based on Novel Fusion Techniques

2020 ◽  
2003 ◽  
Vol 10 (2) ◽  
pp. 111-128 ◽  
Author(s):  
SATORU IKEHARA ◽  
JIN'ICHI MURAKAMI ◽  
YASUHIRO KIMOTO

2021 ◽  
Vol 7 (1) ◽  
pp. 111
Author(s):  
Apriandy Angdresey ◽  
Miguel Angelo Lamongi ◽  
Rinaldi Munir

Information retrieval is used to search for relevant documents so that they can be obtained quickly and precisely. There are many Christians who want to study the gospel. However, often experience problems in finding the Gospel verse and topics dealing with the need to search by the user. Therefore, have to search individually, each verse in the four Gospels to find the topic or verse that the user wants to find out. In this study, the authors used the Bible verses in the Gospels as documents, so that these verses could be searched for the level of relevance or similarity to the entered keywords. Furthermore, to determine the level of relevance between documents and keywords is calculated using the Vector Space Model. Based on the application that has been successfully built, the application can be show 10 documents related to the keywords that are searched and sorted from the most relevant, with the highest similarity value, namely 78.65%.Keywords - Information Retrieval, Vector Space Model, Bible.


2018 ◽  
Vol 76 (5) ◽  
pp. 3590-3601
Author(s):  
Shouqiang Chen ◽  
Yang Chen ◽  
Feng Yuan ◽  
Xiaowei Chang

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