Research on Answer Extraction Method for Domain Question Answering System(QA)

2009 ◽  
Author(s):  
Cun-Li Mao ◽  
Li-Na Li ◽  
Zheng-Tao Yu ◽  
Lu Han ◽  
Jian-Yi Guo ◽  
...  
Keyword(s):  
Extraction Method ◽  
Question Answering ◽  
Question Answering System ◽  
Answer Extraction

A Persian Medical Question Answering System

2020 ◽  
Vol 29 (06) ◽  
pp. 2050019
Author(s):  
Hadi Veisi ◽  
Hamed Fakour Shandi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Main Concept ◽  
Question Answering System ◽  
Detection Algorithms ◽  
Persian Language ◽  
Part Of Speech ◽  
Answer Extraction

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%.

scholarly journals Interactive Document Expansion for Answer Extraction of Question Answering System

2013 ◽  
Vol 22 ◽  
pp. 991-1000 ◽  
Author(s):  
Junichi Fukumoto ◽  
Noriaki Aburai ◽  
Ryosuke Yamanishi
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Answer Extraction
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