scholarly journals Exploring the Web and Semantic Knowledge-Driven Automatic Question Answering System

2018 ◽  
Vol 7 (3.6) ◽  
pp. 379
Author(s):  
S Jayalakshmi ◽  
Ananthi Sheshaayee
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Information Source ◽  
Semantic Knowledge ◽  
High Accuracy ◽  
Question Answering System ◽  
Exact Answer ◽  
Ontology Structure ◽  
The Right ◽  
The Web

The growth of information retrieval from the web sources are increased day by day, proving an effective and efficient way to the user for retrieving relevant documents from the web is an art. Asking the right question and retrieving a right answer to the posted query is a service which provide by the Natural Language Processing. Question Answering System is one of the best ways to identify the candidate answer with high accuracy. The web and Semantic Knowledge Driven Question Answering System (QAS) used to determine the candidate answer for the posted query in the NLP tools.  This method includes Query expansion techniques and entity linking method to identify the information source snippets with ontology structure, also ranking the sentences by applying conditional probability between query and Answer to identify the optimal answer from the web corpus. The result provides an exact answer with high accuracy than the baseline method.  

scholarly journals AN AUTOMATIC WEB-BASED QUESTION ANSWERING SYSTEM FOR E-LEARNING

2017 ◽  
Vol 58 (2) ◽  
pp. 1
Author(s):  
Waheeb Ahmed ◽  
Babu Anto
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Web Based ◽  
Question Answering System ◽  
Natural Language Text ◽  
Exact Answer ◽  
E Learning ◽  
Language Text ◽  
Develop Technique

An automatic web based Question Answering (QA) system is a valuable tool for improving e-learning and education. Several approaches employ natural language processing technology to understand questions given in natural language text, which is incomplete and error-prone. In addition, instead of extracting exact answer, many approaches simply return hyperlinks to documents containing the answers, which is inconvenient for the students or learners. In this paper we develop technique to detect the type of a question, based on which the proper technique for extracting the answer is used. The system returns only blocks or phrases of data containing the answer rather than full documents. Therefore, we can highly improve the efficiency of Web QA systems for e-learning.

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 Math-word embedding in math search and semantic extraction

2020 ◽  
Vol 125 (3) ◽  
pp. 3017-3046 ◽  
Author(s):  
André Greiner-Petter ◽  
Abdou Youssef ◽  
Terry Ruas ◽  
Bruce R. Miller ◽  
Moritz Schubotz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Language Processing ◽  
Digital Library ◽  
Question Answering ◽  
Semantic Knowledge ◽  
Word Embedding ◽  
Mathematical Functions ◽  
Search Tasks ◽  
Math Search

AbstractWord embedding, which represents individual words with semantically fixed-length vectors, has made it possible to successfully apply deep learning to natural language processing tasks such as semantic role-modeling, question answering, and machine translation. As math text consists of natural text, as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding techniques can also be applied to math documents. However, while mathematics is a precise and accurate science, it is usually expressed through imprecise and less accurate descriptions, contributing to the relative dearth of machine learning applications for information retrieval in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in word embedding, it is worthwhile to explore their use and effectiveness in math information retrieval tasks, such as math language processing and semantic knowledge extraction. In this paper, we explore math embedding by testing it on several different scenarios, namely, (1) math-term similarity, (2) analogy, (3) numerical concept-modeling based on the centroid of the keywords that characterize a concept, (4) math search using query expansions, and (5) semantic extraction, i.e., extracting descriptive phrases for math expressions. Due to the lack of benchmarks, our investigations were performed using the arXiv collection of STEM documents and carefully selected illustrations on the Digital Library of Mathematical Functions (DLMF: NIST digital library of mathematical functions. Release 1.0.20 of 2018-09-1, 2018). Our results show that math embedding holds much promise for similarity, analogy, and search tasks. However, we also observed the need for more robust math embedding approaches. Moreover, we explore and discuss fundamental issues that we believe thwart the progress in mathematical information retrieval in the direction of machine learning.

Research and Implementation of Intelligent Question Answering System in MOOC

2014 ◽  
Vol 678 ◽  
pp. 639-643
Author(s):  
Wei Jun Dong ◽  
Guo Hua Geng
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Open Educational Resource ◽  
Agent Model ◽  
Question Answering System ◽  
Passive Learning ◽  
Self Development ◽  
The Status ◽  
Massive Online Open Course

Massive Online Open Course which based on Open Educational Resource might be the most effective method to large-scale quality education, which can realize passive learning to active learning. Analyzing the status and shortages of Intelligent Answering System, propose and design an intelligent question answering system based on agent-model. System use software agents to implement and improve MOOC system’s Intelligent Answering System performance, which has capacity of natural language processing, and good versatility. It can provide an efficient online problem answer environment for thousands of learners, and can effectively promote students' autonomous learning and self-development.

scholarly journals Deep Learning Based Question Answering Search Engine

2021 ◽  
pp. 25-32
Author(s):  
Mrunal Malekar
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language ◽  
Search Engine ◽  
Language Processing ◽  
Question Answering ◽  
Research Work ◽  
Construction Company ◽  
Exact Answer ◽  
Search For Information

Domain based Question Answering is concerned with building systems which provide answers to natural language questions that are asked specific to a domain. It comes under Information Retrieval and Natural language processing. Using Information Retrieval, one can search for the relevant documents which may contain the answer but it won’t give the exact answer for the question asked. In the presented work, a question answering search engine has been developed which first finds out the relevant documents from a huge textual document data of a construction company and then goes a step beyond to extract answer from the extracted document. The robust question answering system developed uses Elastic Search for Information Retrieval [paragraphs extraction] and Deep Learning for answering the question from the short extracted paragraph. It leverages BERT Deep Learning Model to understand the layers and representations between the question and answer. The research work also focuses on how to improve the search accuracy of the Information Retrieval based Elastic Search engine which returns the relevant documents which may contain the answer.

scholarly journals BUILDING QUESTION ANSWERING SYSTEM BASED ON COMPUTING DOMAIN ONTOLOGY

2020 ◽  
Vol 38 (02) ◽  
Author(s):  
TẠ DUY CÔNG CHIẾN
Keyword(s):  
Language Processing ◽  
Digital Libraries ◽  
Question Answering ◽  
Domain Ontology ◽  
Text Documents ◽  
Question Answering System ◽  
Domain Specific ◽  
Sql Database ◽  
Question Answering Systems ◽  
Education Business

Question answering systems are applied to many different fields in recent years, such as education, business, and surveys. The purpose of these systems is to answer automatically the questions or queries of users about some problems. This paper introduces a question answering system is built based on a domain specific ontology. This ontology, which contains the data and the vocabularies related to the computing domain are built from text documents of the ACM Digital Libraries. Consequently, the system only answers the problems pertaining to the information technology domains such as database, network, machine learning, etc. We use the methodologies of Natural Language Processing and domain ontology to build this system. In order to increase performance, I use a graph database to store the computing ontology and apply no-SQL database for querying data of computing ontology.

Aplikasi Tanya Jawab Otomatis Seputar Rekayasa Perangkat Lunak menggunakan Metode Cosine Similarity berbasis Android

Repositor ◽  
2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Rizky Heriawan Prayogo Tanjung ◽  
Maskur Maskur ◽  
Nur Hayatin
Keyword(s):  
Word Processing ◽  
Question Answering ◽  
Processing System ◽  
Cosine Similarity ◽  
Matching Method ◽  
Question Answering System ◽  
Question And Answer ◽  
Kappa Value ◽  
The Right

AbstrakJawaban pertanyaan aplikasi penjawab pertanyaan yang tersedia saat ini masih menggunakan metode pencocokan kata kunci untuk melakukan pencarian atas jawaban. Sistem penjawab pertanyaanotomatis adalah sistem yang secara otomatis mencoba menemukan kembali informasi yang benar untuk pertanyaan diajukan oleh user. Pertanyaan dapat dikembangkan untuk membantu dan membuat lebih mudah untuk menjawab pertanyaan tentang rekayasa perangkat lunak.Aplikasiini menggunakan metodeCosine Similarityyangmerupakan salah satu solusi untukmembantu mencari jawabanpertanyaanyang diinginkan dengantepat,yangbermanfaat untuk sistem pengolah kata. Karena dengan metode ini,tanya jawab otomatis dapat mencari data yang diinginkan oleh penanya,denganmenampilkan jawaban dengan bobot tertinggi sebagai jawaban yang paling tepat.Jawaban pertama atau bobot tertinggi yang dihasilkan oleh sistem adalah jawaban yang benar menurut penilaian sistem dan pakar.Jawaban pertama atau bobot tertinggi yang dihasilkan oleh sistem adalah jawaban yang benar menurut penilaian sistem, pakar dan pengujian Kappa.Hasil pengujian menggunakan kappa statistik memberikan nilai terbaik Kappa pada jawaban pertama (jawaban dengan bobot terbesar).Nilai tersebut membuktikan bahwa sistem yang telah dibangun dapat digunakan untuk mengetahui kemiripan antar kasus penggunaan pertanyaan dan jawaban.AbstractThe Answers of question answering applications that are available today are still using keyword matching method to perform a search for answering. Automatic question answering system is a automatically system used to find information that might correspond to the questions asked by the user. Questions can be developed to help and make it easier to answer questions about software engineering.This application uses the method of Cosine Similarity which is one solution to help searching for the desired answer of questions correctly, that is useful for word processing system. By this method, Automatic Question Answering can looking for desired data of user by showing the the highest weights answer as the best answer.The first or the highest answer resulted by system is the right answer based on system, expert and Kappa Testing. The result of Kappa testing giving the best Kappa value on the first answer (the highest weights answer). It proves that the system can be used to know the similarity between question and answer for between cases of using quetions and answers.

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