scholarly journals Natural Language Query Based Question Answering System

2020 ◽  
Vol 9 (3) ◽  
pp. 2977-2981
Keyword(s):  
Natural Language ◽  
Comparative Study ◽  
Question Answering ◽  
Question Answering System ◽  
Natural Language Query ◽  
User Queries

Development of natural language query based automatic question answering system is in huge demand these days and is a rapidly growing field. It is considered to be the most powerful application for answering different user queries not only on limited domains but also in multi domain environments. In this work, a natural language query based intelligible question answering system is presented that extracts relevant answers from the documents and present the answer in a pre-defined format to the user. A comparative study of the presented model with the traditional techniques is also presented.

Performance of natural language classifiers in a question-answering system

2017 ◽  
Vol 61 (4/5) ◽  
pp. 14:1-14:10 ◽  
Author(s):  
R. Bakis ◽  
D. P. Connors ◽  
P. Dube ◽  
P. Kapanipathi ◽  
A. Kumar ◽  
...  
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Question Answering System

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%.

Liana

1975 ◽  
Vol 30 ◽  
pp. 1-24
Author(s):  
I. Batoni ◽  
R. Henning ◽  
H. Lehmann ◽  
B. Schirmer ◽  
M. Zoeppritz
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Semantic Analysis ◽  
Tree Structure ◽  
Semantic Interpretation ◽  
Syntactic Analysis ◽  
Question Answering System ◽  
Continuous Text ◽  
Language Input

Abstract LIANA is a question answering system in PL/1. The program takes German natural language input and, by morphological, syntactic and semantic analysis, creates a representation of the text, which is stored and can be accessed for retrieval purposes. All individuals (objects) mentioned in the sentence are found and stored. In continuous text, therefore, information about individuals can be piled up successively. LIANA uses the programming concept of the Boston Syntax Analyzer. Therefore, the output of syntactic analysis is a tree structure, simulated through pointers which connect the nodes in the tree. Each node is associated with a feature table which is operated on by the semantic interpretation. Node and feature handling is facilitated by a set of macros for adding, erasing, and checking features and copying, deleting, and inserting nodes.

A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

2017 ◽  
Vol 11 (03) ◽  
pp. 345-371
Author(s):  
Avani Chandurkar ◽  
Ajay Bansal
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Automated System ◽  
Free Form ◽  
Question Answering System ◽  
Novel Approach

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

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