Title of article :
Designing a persian question answering system based on rhetorical structure theory
Author/Authors :
Ehsani, Ali Department of Information Technology Management - Faculty of Management - Science and Research Branch - Islamic Azad University - Tehran, Iran , Amin Mousavi, Abdollah Faculty of Management - Central Tehran Branch - Islamic Azad University - Tehran, Iran , Alborzi, Mahmood Department of Information Technology Management - Faculty of Management - Science and Research Branch - Islamic Azad University - Tehran, Iran , Rastgarpour, Maryam Department of Computer Engineering - Faculty of Computer - Saveh Branch - Islamic Azad University - Tehran, Iran
Abstract :
Background and Objectives: A question answering system answers questions using natural language
processing, a database, or a document set and returns an accurate answer to the user's question.
A large number of efforts have been made to design some systems to answer the user's question.
However, limited studies have been conducted on the Persian language to extract the answer to the
questions with subjects /why" or /how". The scarcity of such studies is attributed to the complexity
and time-consuming analysis and processing of the text structure when going beyond the boundaries
of a sentence.
Methods: The present study's primary purpose was to analyze Persian text to create a set of linguistic
patterns that can perform related information of causal/explanatory text sentences in a general
domain. Information retrieval and text structure recognition algorithms were used for data and text
analysis, called Rhetorical structure theory. In addition, 70 questions for /why" and 20 questions
for /how" were determined for evaluating the system performance, respectively. Finally, the .NET
programming language and relational database, and Persian language interpreters were used to design
the software system.
Results: Eventually, a system was designed and published to answer the question with subjects /why" or /how" with general Data Domain.
Conclusion: The system answered 61 questions with a recall rate of 68%. About 55% of the items
were correctly responded to according to the signs of inter-sentence relation, while the correct answers
to 13% of questions were related to rhetorical relation among the sentences.
Keywords :
Question Answering System , Natural Language Processing , Text mining , causal/explanatory relation , Rhetorical structure theory
Journal title :
International Journal of Nonlinear Analysis and Applications