• Journal of KIISE:Software and Applications
• /
• v.29 no.1_2
• /
• pp.30-41
• /
• 2002

We focus on the mobile agents which are considered as new paradigm to solve information retrieval of large volumes of data in the distributed and heterogeneous environment. The mobile agent moves the computation to data instead of large volumes of data to computations. In this paper, we propose an information retrieval model, which can effectively search data in the distributed and heterogeneous environment, using mobile agents. Our model is applied to the design and implementation of an Q&A(Question and Answer) retrieval system. Our Q&A retrieval system, called QASSMA(Q&A Search System using Mobile Agents), uses mobile agents to retrieve articles from Q&A boards and newsgroups that exist in the heterogeneous and distributed environment. QASSMA has the following features and advantages. First, the mobile retrieval agent moves to the destination server to retrieve articles to reduce the retrieval time by eliminating data traffics from the server to the client host. Also it can reduce the traffic that was occurred in the centralized network system, and reduce the usage of resources by sending its agent and running in the destination host. Finally, the mobile retrieval agent of QASSMA can add and update dynamically the class file according to its retrieval environment, and support other retrieval manner. In this paper, we have shown that our Q&A retrieval system using mobile agents is more efficient than the retrieval system using static agents by our experiments.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.3
• /
• pp.721-726
• /
• 2008

This research suggests real-time people-powered Q&A system that overcoming limitation of natural language handling technology that Q&A system has and demerits that unrelated documents are included in the results of searching in existing information retrieval system and can adapt to change to Web2.0 environment by actively applying users' participation and providing real-time information to users' request of information.

• Journal of KIISE
• /
• v.43 no.10
• /
• pp.1085-1093
• /
• 2016

Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.346-356
• /
• 2022

The jurisprudential legal rules govern the way Muslims react and interact to daily life. This creates a huge stream of questions, that require highly qualified and well-educated individuals, called Muftis. With Muslims representing almost 25% of the planet population, and the scarcity of qualified Muftis, this creates a demand supply problem calling for Automation solutions. This motivates the application of Artificial Intelligence (AI) to solve this problem, which requires a well-designed Question-Answering (QA) system to solve it. In this work, we propose a QA system, based on retrieval augmented generative transformer model for jurisprudential legal question. The main idea in the proposed architecture is the leverage of both state-of-the art transformer models, and the existing knowledge base of legal sources and question-answers. With the sensitivity of the domain in mind, due to its importance in Muslims daily lives, our design balances between exploitation of knowledge bases, and exploration provided by the generative transformer models. We collect a custom data set of 850,000 entries, that includes the question, answer, and category of the question. Our evaluation methodology is based on both quantitative and qualitative methods. We use metrics like BERTScore and METEOR to evaluate the precision and recall of the system. We also provide many qualitative results that show the quality of the generated answers, and how relevant they are to the asked questions.

• Journal of the Korean Society for Library and Information Science
• /
• v.40 no.2
• /
• pp.299-314
• /
• 2006

This study suggests evaluation criteria for the web-based question-answer databases provided by major Korean search portals. In particular, this study suggests evaluation criteria for the relevance of question titles, entire questions, and answer's. The evaluation criteria for the qualify of answers are also developed. Based on these criteria. evaluation of documents from Naver Knowledge-in are performed. The results of this study can be implemented to the development of test collection of question-answer databases. The implications for system designers and web content providers are discussed.

• The Journal of Korean Association of Computer Education
• /
• v.12 no.2
• /
• pp.33-45
• /
• 2009

We can communicate with each other using notice board, questions & answers boards, messages in the web-based learning system, especially questions & answers board are the places that can be shared with the learning experiences between the learners and improve one's learning efficiency. In this study, we found out the problems when studying the learning contents and learning questions & answers boards in the web-based learning system and proposed a web-based learning system consisted of learning contents and the questions & answers boards with ability for searching automatically and providing questions & answers that is related with the learning contents. According to the result of the effectiveness and accuracy analysis, the proposed web-based learning system can be very useful and improve one's learning achievements by searching exactly the learning questions & answers.

• Journal of KIISE
• /
• v.43 no.8
• /
• pp.902-909
• /
• 2016

Question answering system (QA system) is a system that finds an actual answer to the question posed by a user, whereas a typical search engine would only find the links to the relevant documents. Recent works related to the open domain QA systems are receiving much attention in the fields of natural language processing, artificial intelligence, and data mining. However, the prior works on QA systems simply replace all words that are not in the training data with a single token, even though such unseen words are likely to play crucial roles in differentiating the candidate answers from the actual answers. In this paper, we propose a method to compute vectors of such unseen words by taking into account the context in which the words have occurred. Next, we also propose a model which utilizes inverse document frequencies (IDF) to efficiently process unseen words by expanding the system's vocabulary. Finally, we validate that the proposed method and model improve the performance of a QA system through experiments.

• Journal of Digital Contents Society
• /
• v.11 no.3
• /
• pp.375-383
• /
• 2010

In developing question-answering (QA) systems, it is hard to analyze natural language questions syntactically and semantically and to find exact answers to given query questions. In order to avoid these difficulties, we propose a new style of question-answering system that automatically generate natural language queries and can allow to search queries fit for given keywords. The key idea behind generating natural queries is that after significant sentences within text documents are applied to the named entity recognition technique, we can generate a natural query (interrogative sentence) for each named entity (such as person, location, and time). The natural query is divided into two types: simple type and sentence structure type. With the large database of question-answer pairs, the system can easily obtain natural queries and their corresponding answers for given keywords. The most important issue is how to generate meaningful queries which can present unambiguous answers. To this end, we propose two principles to decide which declarative sentences can be the sources of natural queries and a pattern-based method for generating meaningful queries from the selected sentences.

• Journal of KIISE:Computing Practices and Letters
• /
• v.10 no.6
• /
• pp.529-536
• /
• 2004

The main idea of this paper is to combine position information in sentence and query type classification to make the documents ranking to query more accessible. First, the use of conceptual graphs for the representation of document contents In information retrieval is discussed. The method is based on well-known strategies of text comparison, such as Dice Coefficient, with position-based weighted term. Second, we introduce a method for learning query type classification that improves the ability to retrieve answers to questions from Question Answering system. Proposed methods employ naive bayes classification in machine learning fields. And, we used a collection of approximately 30,000 question-answer pairs for training, obtained from Frequently Asked Question(FAQ) files on various subjects. The evaluation on a set of queries from international TREC-9 question answering track shows that the method with machine learning outperforms the underline other systems in TREC-9 (0.29 for mean reciprocal rank and 55.1% for precision).

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.12
• /
• pp.35-48
• /
• 2017

Extracting temporal information from documents is becoming more important, because it can be used to various applications such as Question-Answering (QA) systems, Recommendation systems, or Information Retrieval (IR) systems. Most previous studies only focus on English documents, and they are not applicable to the other languages due to the inherent characteristics of languages. In this paper, we propose a new system, named ExoTime, designed to extract temporal information from Korean documents. The ExoTime adopts an external Knowledge Base (KB) in order to achieve better prediction performance, and it also applies a bagging method to the temporal relation prediction. We show that the effectiveness of the proposed approaches by empirical results using Korean TimeBank. The ExoTime system works as a part of ExoBrain that is an artificial intelligent QA system.