Rule Based Part of Speech Tagger for Arabic Question Answering System

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%.

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CONSTRUCTING AN ONTOLOGY-BASED AND GRAPH-BASED KNOWLEDGE REPRESENTATION OF ENGLISH QURAN

Jurnal Teknologi ◽

10.11113/jt.v78.9539 ◽

2016 ◽

Vol 78 (8-2) ◽

Author(s):

Mohamad Fauzan Noordin ◽

Tengku Mohd. Tengku Sembok ◽

Roslina Othman ◽

Ria Hari Gusmita

Keyword(s):

Information Retrieval ◽

Knowledge Representation ◽

Question Answering ◽

Graph Database ◽

Retrieval Process ◽

Valuable Contribution ◽

Question Answering System ◽

Part Of Speech

This paper describes a work in constructing two models of knowledge representation (KR) in aiming to do evaluation of their achievement in contributing to increase performance of retrieving information on English Quran domain. Due to many approaches available to construct a KR in providing data for information retrieval process, there is a need to find out in what model the KR could provide a valuable contribution for retrieving information. We focused on ontology-based KR and graph database-based KR. We use Quranic Arabic corpus that available at http://www.corpus.quran.com as a source to build the KR. We extracted several data from it i.e. English token, token location, and token Part of Speech (POS). Protégé is used to construct the ontology and Neo4j is utilized in developing the graph database. Both KR models will be equipped in developing of an English Quran Question Answering system in order to evaluate their benefit.

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Part-of-speech and postion attention mechanism based BLSTM for question answering system

2018 International Conference on Image and Video Processing, and Artificial Intelligence ◽

10.1117/12.2326940 ◽

2018 ◽

Author(s):

Hao Liu ◽

GuangPing Zeng ◽

Qingchuan Zhang ◽

Liu Chen

Keyword(s):

Question Answering ◽

Attention Mechanism ◽

Question Answering System ◽

Part Of Speech

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A Rule-based Chinese Question Answering System for Reading Comprehension Tests

10.1109/iih-msp.2007.59 ◽

2007 ◽

Cited By ~ 5

Author(s):

Xiaoyan Hao ◽

Xiaoming Chang ◽

Kaiying Liu

Keyword(s):

Reading Comprehension ◽

Question Answering ◽

Rule Based ◽

Question Answering System ◽

Comprehension Tests ◽

Reading Comprehension Tests

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A Rule Based Question Answering System in Malayalam Corpus Using Vibhakthi and POS Tag Analysis

Procedia Technology ◽

10.1016/j.protcy.2016.05.124 ◽

2016 ◽

Vol 24 ◽

pp. 1534-1541 ◽

Cited By ~ 2

Author(s):

S.M. Archana ◽

Naima Vahab ◽

Rekha Thankappan ◽

C. Raseek

Keyword(s):

Question Answering ◽

Rule Based ◽

Question Answering System

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A rule-based question answering system on relevant documents of Indonesian Quran Translation

2014 International Conference on Cyber and IT Service Management (CITSM) ◽

10.1109/citsm.2014.7042185 ◽

2014 ◽

Cited By ~ 7

Author(s):

Ria Hari Gusmita ◽

Yusuf Durachman ◽

Salman Harun ◽

Asep Fajar Firmansyah ◽

Husni Teja Sukmana ◽

...

Keyword(s):

Question Answering ◽

Rule Based ◽

Question Answering System

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QUESTION ANSWERING SYSTEM INFORMASI PARIWISATA KOTA PALEMBANG

Jurnal Ilmiah Matrik ◽

10.33557/jurnalmatrik.v21i2.566 ◽

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%.

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

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d6773.118419 ◽

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.

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