Question Analysis for a Community-Based Vietnamese Question Answering System

Author(s):  
Quan Hung Tran ◽  
Minh Le Nguyen ◽  
Son Bao Pham
2020 ◽  
Vol 20 (1) ◽  
pp. 112-128
Author(s):  
Ngo Xuan Bach ◽  
Phan Duc Thanh ◽  
Tran Thi Oanh

AbstractBuilding a computer system, which can automatically answer questions in the human language, speech or text, is a long-standing goal of the Artificial Intelligence (AI) field. Question analysis, the task of extracting important information from the input question, is the first and crucial step towards a question answering system. In this paper, we focus on the task of Vietnamese question analysis in the education domain. Our goal is to extract important information expressed by named entities in an input question, such as university names, campus names, major names, and teacher names. We present several extraction models that utilize the advantages of both traditional statistical methods with handcrafted features and more recent advanced deep neural networks with automatically learned features. Our best model achieves 88.11% in the F1 score on a corpus consisting of 3,600 Vietnamese questions collected from the fan page of the International School, Vietnam National University, Hanoi.


Author(s):  
Caner Derici ◽  
Kerem Çelik ◽  
Ekrem Kutbay ◽  
Yiğit Aydın ◽  
Tunga Güngör ◽  
...  

Author(s):  
Quan Hung Tran ◽  
Nien Dinh Nguyen ◽  
Kien Duc Do ◽  
Thinh Khanh Nguyen ◽  
Dang Hai Tran ◽  
...  

The first phase of the question answering process is the detailed process of the question analysis. Thus it analyzes what kind of question and how it can be answered. Question analysis uses the parsing and semantic analysis of the dataset. Thus this journal gives the entire impact on how an answer is perfectly retrieved and prioritised


2015 ◽  
Vol 9 ◽  
pp. 6491-6505
Author(s):  
Syarilla I. Ahmad Saany ◽  
Ali Mamat ◽  
Aida Mustapha ◽  
Lilly S. Affendey ◽  
M. Nordin A. Rahman

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval


Sign in / Sign up

Export Citation Format

Share Document