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 Natural language processing versus content-based image analysis for medical document retrieval

2009 ◽  
Vol 60 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Aurélie Névéol ◽  
Thomas M. Deserno ◽  
Stéfan J. Darmoni ◽  
Mark Oliver Güld ◽  
Alan R. Aronson
Keyword(s):  
Image Analysis ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Document Retrieval ◽  
Versus Content ◽  
Medical Document

Pendekatan Ontologi Dalam Capaian Dan Perwakilan Semantik Dokumen Web

2012 ◽  
Author(s):  
Arifah Che Alhadi ◽  
Shahrul Azman Noah ◽  
Lailatul Qadri Zakaria
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Semantic Information ◽  
Information Access ◽  
Document Retrieval ◽  
Integration Model ◽  
Web Documents ◽  
Semantic Query ◽  
Language Analysis ◽  
Natural Language Analysis

Visi web semantik membolehkan capaian maklumat dilakukan secara semantik, yang mana model semantik kueri dipadankan dengan maklumat semantik dokumen web. Namun demikian kebanyakan dokumen web adalah tidak berstruktur dan tidak mempunyai maklumat semantik dokumen menyebabkan kesukaran proses pengkuerian. Oleh itu, capaian dan pengekstrakan maklumat semantik daripada dokumen web adalah amat penting dalam merealisasikan visi web semantik dan meningkatkan kualiti capaian maklumat. Kertas kerja ini membincangkan pengaplikasian pendekatan ontologi spesifik dan pemprosesan bahasa tabii dalam menyokong capaian dan pengekstrakan maklumat semantik dokumen web. Dengan menggunakan kedua-dua teknik ini, setiap kali capaian maklumat dilakukan, sistem akan menjana model integrasi semantik dokumen iaitu dokumen yang dicapai oleh enjin gelintar komersial yang ditetapkan. Model intergrasi semantik dokumen ini membolehkan pengguna mencapai dan melayarinya secara semantik. Hasil pengujian capaian dan padanan konsep yang dijalankan memperlihatkan kedua-dua teknik yang digunakan mampu mengenal pasti dan mengekstrak maklumat semantik daripada kueri dan kandungan dokumen web. Kata kunci: Capaian dokumen semantik, web semantik, ontologi, analisis bahasa tabii, perwakilan semantik dokumen, perwakilan semantik kueri The Semantic Web vision offers the potential to express queries in a more semantically way whereby semantic query model will be matched with semantic information of the document. However, the unstructured natures of existing web documents, which lack of semantic prove to be a difficult task for such a query. Therefore, semantic information retrieval and semantic information extraction of web documents content are important to realize semantic web vision and enhance the quality of information access. To support this, the semantic information content of web documents need to be specified in order to make the tangled information more structured and accessible. In this paper, we propose an approach meant to semantically query web documents using natural language analysis technique and a domain specific ontology. Using both techniques, the tool gradually constructs the semantic document integration model of the documents retrieved from an existing search engine for each search session. The semantic model can then be semantically refined and browsed by the user. The result of concept matching and accessing shows that both techniques that have been used could identify and extract semantic information from query and web document content. Key words: Semantic document retrieval, semantic web, ontology, natural language analysis, semantic document representation, semantic query representation

scholarly journals Decision Support, Knowledge Representation and Management: Structuring Knowledge for Better Access

2008 ◽  
Vol 17 (01) ◽  
pp. 80-82 ◽  
Author(s):  
A.-M. Rassinoux ◽  
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Methodological Approach ◽  
Expressive Power ◽  
Clinical Decision ◽  
Document Retrieval ◽  
Wide Range ◽  
Structuring Knowledge

Summary Objectives To summarize current outstanding research in the field of decision support, knowledge representation and management. Method Synopsis of the articles selected for the IMIA Yearbook 2008. Results Five papers from international peer reviewed journals have been selected for the section on decision support, knowledge representation and management. They address a wide range of topics such as the recognition and extraction of negation or time from clinical narratives, the use of ontological elements to reduce the complexity of natural language processing applications or to strengthen the precision of document retrieval as well as the benefits of integrating clinical decision support within computer provider orderentry. Conclusions The best paper selection brings to light that whatever the methodological approach used in decision support, knowledge representation and management, all applications benefit from manipulating information that is expressed in both a meaningful and structured way. In order to combine the flexibility and expressive power of natural language with the computational tractability of structured data, the electronic health record based on structured narrative offers new perspectives.

Multilingual Access to Document Databases

2013 ◽  
Author(s):  
Steven A. Pollitt ◽  
Geoff Ellis
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Expert Systems ◽  
Document Retrieval ◽  
Optimal Solutions ◽  
End User ◽  
Natural Language Query ◽  
Document Databases

This paper examines the reasons why approaches to facilitate document retrieval which apply AI (Artificial Intelligence) or Expert Systems techniques, relying on so-called "natural language" query statements from the end-user will result in sub-optimal solutions. It does so by reflecting on the nature of language and the fundamental problems in document retrieval.

An analysis of ill-formed input in natural language queries to document retrieval systems

1991 ◽  
Vol 27 (6) ◽  
pp. 615-622 ◽  
Author(s):  
Charlene W. Young ◽  
Caroline M. Eastman ◽  
Robert L. Oakman
Keyword(s):  
Natural Language ◽  
Document Retrieval ◽  
Retrieval Systems

scholarly journals AUTO STEMMING OF AZERBAIJANI LANGUAGE

2019 ◽  
Vol 10 (1) ◽  
pp. 59-66
Author(s):  
Morteza Hasan Alizadeh ◽  
◽  
Seyyed Amin Seyyedi ◽  
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Finite Automaton ◽  
Document Retrieval ◽  
Experimental Result ◽  
Deterministic Finite Automaton ◽  
Web Based ◽  
Structural Methods

One of important features in natural language processing is to find the root of a word. Stemming means to remove prefixes, suffixes, and infixes for finding the root of the word. Its aims are about to information retrieval, exploring text, machine for translation, and word look up based on its root. Stemming increases document retrieval by 10-50% in most of international languages, it also compresses the size of web-based table indexes documents up to 50%. In this paper, by analyzing stemming approaches, using structural methods, and deterministic finite automaton machine, applying 274 existing prefixes in language (linkage), a stemming system for Azerbaijani language is generated. Experimental result demonstrates that the proposed algorithm performs more than 97% accuracy.

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