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 QUASE: AN Ontology-Based Domain Specific Natural Language Question Answering System

2019 ◽  
Vol 8 (4) ◽  
pp. 261-268
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Closed Domain ◽  
Question Answering System ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Natural Language Question ◽  
Finite Set ◽  
Language Question

Since early days Question Answering (QA) has been an intuitive way of understanding the concept by humans. Considering its inevitable importance it has been introduced to children from very early age and they are promoted to ask more and more questions. With the progress in Machine Learning & Ontological semantics, Natural Language Question Answering (NLQA) has gained more popularity in recent years. In this paper QUASE (QUestion Answering System for Education) question answering system for answering natural language questions has been proposed which help to find answer for any given question in a closed domain containing finite set of documents. Th e QA s y st em m a inl y focuses on factoid questions. QUASE has used Question Taxonomy for Question Classification. Several Natural Language Processing techniques like Part of Speech (POS) tagging, Lemmatization, Sentence Tokenization have been applied for document processing to make search better and faster. DBPedia ontology has been used to validate the candidate answers. By application of this system the learners can gain knowledge on their own by getting precise answers to their questions asked in natural language instead of getting back merely a list of documents. The precision, recall and F measure metrics have been taken into account to evaluate the performance of answer type evaluation. The metric Mean Reciprocal Rank has been considered to evaluate the performance of QA system. Our experiment has shown significant improvement in classifying the questions in to correct answer types over other methods with approximately 91% accuracy and also providing better performance as a QA system in closed domain search.

Question Answering

2012 ◽  
Author(s):  
Sanda Harabagiu ◽  
Dan Moldovan
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Semantic Features ◽  
Knowledge Based ◽  
Answer Extraction ◽  
Small Set ◽  
Processing Module ◽  
On Line ◽  
Processing Techniques

Textual Question Answering (QA) identifies the answer to a question in large collections of on-line documents. By providing a small set of exact answers to questions, QA takes a step closer to information retrieval rather than document retrieval. A QA system comprises three modules: a question-processing module, a document-processing module, and an answer extraction and formulation module. Questions may be asked about any topic, in contrast with Information Extraction (IE), which identifies textual information relevant only to a predefined set of events and entities. The natural language processing (NLP) techniques used in open-domain QA systems may range from simple lexical and semantic disambiguation of question stems to complex processing that combines syntactic and semantic features of the questions with pragmatic information derived from the context of candidate answers. This article reviews current research in integrating knowledge-based NLP methods with shallow processing techniques for QA.

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.

Machine learning for query formulation in question answering

2011 ◽  
Vol 17 (4) ◽  
pp. 425-454 ◽  
Author(s):  
CHRISTOF MONZ
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Machine Learning Techniques ◽  
Strong Impact ◽  
Query Formulation ◽  
Answer Extraction ◽  
Question Answering Systems

AbstractResearch on question answering dates back to the 1960s but has more recently been revisited as part of TREC's evaluation campaigns, where question answering is addressed as a subarea of information retrieval that focuses on specific answers to a user's information need. Whereas document retrieval systems aim to return the documents that are most relevant to a user's query, question answering systems aim to return actual answers to a users question. Despite this difference, question answering systems rely on information retrieval components to identify documents that contain an answer to a user's question. The computationally more expensive answer extraction methods are then applied only to this subset of documents that are likely to contain an answer. As information retrieval methods are used to filter the documents in the collection, the performance of this component is critical as documents that are not retrieved are not analyzed by the answer extraction component. The formulation of queries that are used for retrieving those documents has a strong impact on the effectiveness of the retrieval component. In this paper, we focus on predicting the importance of terms from the original question. We use model tree machine learning techniques in order to assign weights to query terms according to their usefulness for identifying documents that contain an answer. Term weights are learned by inspecting a large number of query formulation variations and their respective accuracy in identifying documents containing an answer. Several linguistic features are used for building the models, including part-of-speech tags, degree of connectivity in the dependency parse tree of the question, and ontological information. All of these features are extracted automatically by using several natural language processing tools. Incorporating the learned weights into a state-of-the-art retrieval system results in statistically significant improvements in identifying answer-bearing documents.

scholarly journals Ontology-Based Question Answering System for an Academic Domain

2021 ◽  
Author(s):  
García-Robledo Gabriela A ◽  
Reyes-Ortiz José A ◽  
González-Beltrán Beatriz A ◽  
Bravo Maricela
Keyword(s):  
Natural Language Processing ◽  
User Interface ◽  
Speech Recognition ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Question Answering System ◽  
Methods And Techniques

The development of question answering (QA) systems involves methods and techniques from the areas of Information Extraction (EI), Natural Language Processing (NLP), and sometimes speech recognition. A user interface that involves all these tasks requires deep development to improve the interaction between a user and a device. This paper describes a Spanish QA system for an academic domain through a multi-platform user interface. The system uses a voice query to be transformed into text. The semi-structured query is converted into SQWRL language to extract a system of ontologies from an academic domain using patterns. The answer of the ontologies is placed in templates classified according to the type of question. Finally, the answer is transformed into a voice. A method for experimentation is presented focusing on the questions asked in voice and their respective answers by experts from the academic domain in a set of 258 questions, obtaining a 92% accuracy.

scholarly journals QUESTION ANSWERING SYSTEM INFORMASI PARIWISATA KOTA PALEMBANG

2019 ◽  
Vol 21 (2) ◽  
pp. 128-138
Author(s):  
Marga Lenni ◽  
R. Kristoforus Jawa Bendi
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Specific Information ◽  
Natural Languages ◽  
Question Answering System ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Result Show ◽  
Simple Question

The development of information technology is very rapid, resulting in an overflow of data. The amount of data can be used to obtain information needed by the user. The problem is, not all information can be found easily, especially very specific information. Likewise information about tourism. One way to overcome these problems is to utilize Natural Language Processing Technology, especially Question Answering System, which allows Computers to understand the meaning of Questions posed by users in natural languages. This study built a simple Question Answering System application. Application developed with PHP programming language, and MySql database. Preprocessing techniques used are Tokenization, Part-Of-Speech tagging, and Named Entity Recognation. The test result show that the application is able to provide answers to user questions of 82,05%.

SWFQA Semantic Web Based Framework for Question Answering

2019 ◽  
Vol 9 (1) ◽  
pp. 88-106
Author(s):  
Irphan Ali ◽  
Divakar Yadav ◽  
Ashok Kumar Sharma
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Knowledge Bases ◽  
Digital Information ◽  
Web Based ◽  
Question Answering System ◽  
User Query

A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Answer Relevance Score” to find the relevance of each answer returned by the system. The results obtained thereof are quite promising. The real-time performance of the system has been evaluated on the answers, extracted from the knowledge base.

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