Case based Indonesian closed domain question answering system with real world questions

This work aims to propose a system for the Algerian Fatawa House in orderto facilitate the task of the Expert Mufti who is responsible of giving fatawa for Algerian people inquiries. In fact, as this house is recent and does not have sufficient human resources, it is difficult to satisfy all inquiries coming daily, this leads the askers to wait for a long time before getting answers. The proposed system allows the askers to express concerns they may have. By using a case-based reasoning mechanism combined with ontology domain, the system tries to retrieve similar cases from the knowledge base. In the casewhere the response already exists, the system immediately provides the answer to the askers. Otherwise, an inquery is automatically formulated and sent to the expert Mufti-which is a certified scholar-in order either to validate the generated response by the system or give a new answer. Such a question-answering system may be very helpful for askers to get their answers faster since it allows both the storage of previous <em>fatawas</em> and their retrieval for processing coming inquiries. To validate our proposal, we rely on <em>fatawas</em> concerning the Islamic finance and banking transactions domain. Overall, the results are encouraging and satisfactory.

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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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A relational tsetlin machine with applications to natural language understanding

Journal of Intelligent Information Systems ◽

10.1007/s10844-021-00682-5 ◽

2022 ◽

Author(s):

Rupsa Saha ◽

Ole-Christoffer Granmo ◽

Vladimir I. Zadorozhny ◽

Morten Goodwin

Keyword(s):

Natural Language ◽

Real World ◽

Question Answering ◽

Logic Program ◽

Closed Domain ◽

Computing Power ◽

First Order ◽

Finite State ◽

Order Representation ◽

Pattern Recognition Approach

AbstractTsetlin machines (TMs) are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM is relational and can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, the first-order representation produces 10 × more compact KBs, along with an increase in answering accuracy from 94.83% to 99.48%. The approach is further robust towards erroneous, missing, and superfluous information, distilling the aspects of a text that are important for real-world understanding

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AQUA: A Closed-Domain Question Answering System

Information Systems Management ◽

10.1080/10580530.2010.493825 ◽

2010 ◽

Vol 27 (3) ◽

pp. 217-225 ◽

Cited By ~ 7

Author(s):

Maria Vargas-Vera ◽

Miltiadis D. Lytras

Keyword(s):

Question Answering ◽

Closed Domain ◽

Question Answering System

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Case Based Reasioning (CBR) for Medical Question Answering System

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v3i2.263 ◽

2018 ◽

pp. 113-118 ◽

Cited By ~ 3

Author(s):

Setio Basuki ◽

Alfira Rizky ◽

Galih Wasis Wicaksono

Keyword(s):

Test Data ◽

Question Answering ◽

Case Based Reasoning ◽

Question Answering System ◽

Matching Process ◽

Validation Testing ◽

Expert Validation ◽

System Knowledge ◽

Case Based

In this research, the researchers implement a medical Question Answering System (QAS), a complaint system in the form of sentences or paragraphs of questions about the complaint (illness) suffered by a person. Afterwards, the system will give answer to the questions with answers in the form of diagnosis based on the system knowledge. The system in this study has knowledge of the system obtained based on Case Based Reasoning (CBR) method from the previous cases stored in the database. When there is a new case, the system will perform a matching process using CBR and Sorenson Coefficient calculations to find out which the previous cases have the highest percentage of matches with the new case. Then the selected previous cases will be taken and given to the new case. Testing is processed by using 2 types of testing, expert validation testing with result of 28 data of appropriate test from 30 test data and accuracy testing resulting of 93,33% from the appropriate test data.

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