• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.35-48
• /
• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.73-85
• /
• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.79-104
• /
• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Communications of Mathematical Education
• /
• v.24 no.1
• /
• pp.235-257
• /
• 2010

Contemporary belief is that the creative talented can create new knowledge and lead national development, so lots of countries in the world have interest in Gifted Education. As we well know, U.S.A., England, Russia, Germany, Australia, Israel, and Singapore enforce related laws in Gifted Education to offer Gifted Classes, and our government has also created an Improvement Act in January, 2000 and Enforcement Ordinance for Gifted Improvement Act was also announced in April, 2002. Through this initiation Gifted Education can be possible. Enforcement Ordinance was revised in October, 2008. The main purpose of this revision was to expand the opportunity of Gifted Education to students with special education needs. One of these programs is, the opportunity of Gifted Education to be offered to lots of the Gifted by establishing Special Classes at each school. Also, it is important that the quality of Gifted Education should be combined with the expansion of opportunity for the Gifted. Social opinion is that it will be reckless only to expand the opportunity for the Gifted Education, therefore, assessment on the Teaching and Learning Program for the Gifted is indispensible. In this study, 3 middle schools were selected for the Teaching and Learning Programs in mathematics. Each 1st Grade was reviewed and analyzed through comparative tables between Regular and Gifted Education Programs. Also reviewed was the content of what should be taught, and programs were evaluated on assessment standards which were revised and modified from the present teaching and learning programs in mathematics. Below, research issues were set up to assess the formation of content areas and appropriateness for Teaching and Learning Programs for the Gifted in mathematics. A. Is the formation of special class content areas complying with the 7th national curriculum? 1. Which content areas of regular curriculum is applied in this program? 2. Among Enrichment and Selection in Curriculum for the Gifted, which one is applied in this programs? 3. Are the content areas organized and performed properly? B. Are the Programs for the Gifted appropriate? 1. Are the Educational goals of the Programs aligned with that of Gifted Education in mathematics? 2. Does the content of each program reflect characteristics of mathematical Gifted students and express their mathematical talents? 3. Are Teaching and Learning models and methods diverse enough to express their talents? 4. Can the assessment on each program reflect the Learning goals and content, and enhance Gifted students' thinking ability? The conclusions are as follows: First, the best contents to be taught to the mathematical Gifted were found to be the Numeration, Arithmetic, Geometry, Measurement, Probability, Statistics, Letter and Expression. Also, Enrichment area and Selection area within the curriculum for the Gifted were offered in many ways so that their Giftedness could be fully enhanced. Second, the educational goals of Teaching and Learning Programs for the mathematical Gifted students were in accordance with the directions of mathematical education and philosophy. Also, it reflected that their research ability was successful in reaching the educational goals of improving creativity, thinking ability, problem-solving ability, all of which are required in the set curriculum. In order to accomplish the goals, visualization, symbolization, phasing and exploring strategies were used effectively. Many different of lecturing types, cooperative learning, discovery learning were applied to accomplish the Teaching and Learning model goals. For Teaching and Learning activities, various strategies and models were used to express the students' talents. These activities included experiments, exploration, application, estimation, guess, discussion (conjecture and refutation) reconsideration and so on. There were no mention to the students about evaluation and paper exams. While the program activities were being performed, educational goals and assessment methods were reflected, that is, products, performance assessment, and portfolio were mainly used rather than just paper assessment.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.57-79
• /
• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.

• The Korean Journal of Archival Studies
• /
• no.22
• /
• pp.157-199
• /
• 2009

The organization for managing records and archives did not emerge together with the founding of the Chinese Communist Party. Such management became active with the establishment of the Department of Documents (文書科) and its affiliated offices overseeing reading and safekeeping of official papers, after the formation of the Central Secretariat(中央秘書處) in 1926. Improving the work of the Secretariat's organization became the focus of critical discussions in the early 1930s. The main criticism was that the Secretariat had failed to be cognizant of its political role and degenerated into a mere "functional organization." The solution to this was the "politicization of the Secretariat's work." Moreover, influenced by the "Rectification Movement" in the 1940s, the party emphasized the responsibility of the Resources Department (材料科) that extended beyond managing documents to collecting, organizing and providing various kinds of important information data. In the mean time, maintaining security with regard to composing documents continued to be emphasized through such methods as using different names for figures and organizations or employing special inks for document production. In addition, communications between the central political organs and regional offices were emphasized through regular reports on work activities and situations of the local areas. The General Secretary not only composed the drafts of the major official documents but also handled the reading and examination of all documents, and thus played a central role in record processing. The records, called archives after undergoing document processing, were placed in safekeeping. This function was handled by the "Document Safekeeping Office(文件保管處)" of the Central Secretariat's Department of Documents. Although the Document Safekeeping Office, also called the "Central Repository(中央文庫)", could no longer accept, beginning in the early 1930s, additional archive transfers, the Resources Department continued to strengthen throughout the 1940s its role of safekeeping and providing documents and publication materials. In particular, collections of materials for research and study were carried out, and with the recovery of regions which had been under the Japanese rule, massive amounts of archive and document materials were collected. After being stipulated by rules in 1931, the archive classification and cataloguing methods became actively systematized, especially in the 1940s. Basically, "subject" classification methods and fundamental cataloguing techniques were adopted. The principle of assuming "importance" and "confidentiality" as the criteria of management emerged from a relatively early period, but the concept or process of evaluation that differentiated preservation and discarding of documents was not clear. While implementing a system of secure management and restricted access for confidential information, the critical view on providing use of archive materials was very strong, as can be seen in the slogan, "the unification of preservation and use." Even during the revolutionary movement and wars, the Chinese Communist Party continued their efforts to strengthen management and preservation of records & archives. The results were not always desirable nor were there any reasons for such experiences to lead to stable development. The historical conditions in which the Chinese Communist Party found itself probably made it inevitable. The most pronounced characteristics of this process can be found in the fact that they not only pursued efficiency of records & archives management at the functional level but, while strengthening their self-awareness of the political significance impacting the Chinese Communist Party's revolution movement, they also paid attention to the value possessed by archive materials as actual evidence for revolutionary policy research and as historical evidence of the Chinese Communist Party.

• Korean Journal of Heritage: History & Science
• /
• v.50 no.4
• /
• pp.4-19
• /
• 2017

Kim Jeong-gi (pen-name: Changsan, Mar. 31, 1930 - Aug. 26, 2015) made a major breakthrough in the history of cultural property excavation in Korea: In 1959, he began to develop an interest in cultural heritage after starting work as an employee of the National Museum of Korea. For about thirty years until he retired from the National Research Institute of Cultural Heritage in 1987, he devoted his life to the excavation of our country's historical relics and artifacts and compiled countless data about them. He continued striving to identify the unique value and meaning of our cultural heritage in universities and excavation organizations until he passed away in 2015. Changsan spearheaded all of Korea's monumental archeological excavations and research. He is widely known at home and abroad as a scholar of Korean archeology, particularly in the early years of its existence as an academic discipline. As such, he has had a considerable influence on the development of Korean archeology. Although his multiple activities and roles are meaningful in terms of the country's archaeological history, there are limits to his contributions nevertheless. The Deoksugung Palace period (1955-1972), when the National Museum of Korea was situated in Deoksugung Palace, is considered to be a time of great significance for Korean archeology, as relics with diverse characteristics were researched during this period. Changsan actively participated in archeological surveys of prehistoric shell mounds and dwellings, conducted surveys of historical relics, measured many historical sites, and took charge of photographing and drawing such relics. He put to good use all the excavation techniques that he had learned in Japan, while his countrywide archaeological surveys are highly regarded in terms of academic history as well. What particularly sets his perspectives apart in archaeological terms is the fact that he raised the possibility of underwater tombs in ancient times, and also coined the term "Haemi Culture" as part of a theory of local culture aimed at furthering understanding of Bronze Age cultures in Korea. His input was simply breathtaking. In 1969, the National Research Institute of Cultural Heritage (NRICH) was founded and Changsan was appointed as its head. Despite the many difficulties he faced in running the institute with limited financial and human resources, he gave everything he had to research and field studies of the brilliant cultural heritages that Korea has preserved for so long. Changsan succeeded in restoring Bulguksa Temple, and followed this up with the successful excavation of the Cheonmachong Tomb and the Hwangnamdaechong Tomb in Gyeongju. He then explored the Hwangnyongsa Temple site, Bunhwangsa Temple, and the Mireuksa Temple site in order to systematically evaluate the Buddhist culture and structures of the Three Kingdoms Period. We can safely say that the large excavation projects that he organized and carried out at that time not only laid the foundations for Korean archeology but also made significant contributions to studies in related fields. Above all, in terms of the developmental process of Korean archeology, the achievements he generated with his exceptional passion during the period are almost too numerous to mention, but they include his systematization of various excavation methods, cultivation of archaeologists, popularization of archeological excavations, formalization of survey records, and promotion of data disclosure. On the other hand, although this "Excavation King" devoted himself to excavations, kept precise records, and paid keen attention to every detail, he failed to overcome the limitations of his era in the process of defining the nature of cultural remains and interpreting historical sites and structures. Despite his many roles in Korean archeology, the fact that he left behind a controversy over the identity of the occupant of the Hwangnamdaechong Tomb remains a sore spot in his otherwise perfect reputation.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.37 no.2
• /
• pp.74-88
• /
• 2019

This study developed the Assessment Indicator evaluating eco-cultural value of temple forest in Korea and applied the developed Assessment Indicator to Songgwang-sa(also known as Seungbo-sachal), one of the Three Jewels Temple. Literature reviews and the draft of Assessment Indicator were drawn from brainstorming(including 2 forest therapy experts, 1 Buddhist monk expert, 1 landscape architect, 1 forest expert, and 6 researchers). After that, the Assessment Indicator drawn from the group of experts(the 1st in-depth interview: 32 people, the 2nd in-depth interview: 30 people) was verified and revised. The final Assessment Indicator, which was composed of 4 parts and 20 items, was developed. The results are as follows. The eco-cultural Assessment Indicator of temple forest was composed of 4 parts, which were Historical Cultural value, Ecological value, Recreatory Visitational value, and Educational Useful value, and 20 items and each item had 5 points. Historical Cultural value had 5 items and its total points were 25. Ecological value had 5 items and had total 25 points. Recreatory Visitational value had 6 items, 30 total points. Educational Useful value had 4 items, 20 total points. The total points of the eco-cultural Assessment Indicator were 100 points. As a result of applying the developed Assessment Indicator to the target place, Songgwang-sa in Mt. Jogye, Historical Cultural value of temple forest was calculated as 23 points(out of 25). Ecological value was 21 point(out of 25), Recreatory Visitational value, 22 points(out of 30), and Educational Useful value, 16 points(out of 20). The total points were 82(out of 100). Consequently, this study is meaningful based on the following 5 aspects. Firstly, this study challenged the development of the eco-cultural Assessment Indicator of temple forest for the first time. It is significant because the developed Assessment Indicator can be a useful resource for the eco-cultural value of temple forest. Secondly, the result showed that Educational Useful value and Recreatory Visitational value of forest temple were very low. Therefore, the supports for leisure, tour, education, and use of temple forest are needed from Korea Forest Service, Ministry of Environment, Cultural Heritage Administration and other government agencies since they acknowledge the temple forest as the best customers in Korea. Thirdly, the excellence or for eco-cultural value of temple forest needs to be extended in a national level. It is possible to make a Korean National Bran(e.g., the Therapy at the Temple) by blending temple stay, which is only in temples, and therapy, and is also possible to be a global tour industry. Fourthly, this study suggested legal definition about the necessary of legal definition for temple forest because there is no legal definition on temple forest in the current situation. When the definition of temple forest is legally arranaged, it would be a foundation for conserving eco-cultural value of temple forest, for organizing exclusively responsible departments in governmental institutions, and further for registering temple forest as World Natural Heritage. Lastly, the developed eco-cultural Assessment Indicators of temple forest from this study would be applied to "the 7 Sansa, Buddhist Mountain Monasteries in Korea(Sansa)" and the characteristics of each 7 temple are drawn. This study would be a basic data for temples' management and use with the eco-cultural Assessment Indicator of temple forest.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.111-136
• /
• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.23 no.1
• /
• pp.35-39
• /
• 2019

Purpose Hepatitis B virus (hepatitis B virus, HBV) infection is a worldwide major public health problem and it is known as a major cause of chronic hepatitis, liver cirrhosis and liver cancer. And serologic tests of hepatitis B virus is essential for diagnosing and treating these diseases. In addition, with the development of molecular diagnostics, the detection of HBV DNA in serum diagnoses HBV infection and is recognized as an important indicator for the antiviral agent treatment response assessment. We performed HBeAg assay using Immunoradiometric assay (IRMA) and Chemiluminescent Microparticle Immunoassay (CMIA) in hepatitis B patients treated with antiviral agents. The detection rate of HBV DNA in serum was measured and compared by RT-PCR (Real Time - Polymerase Chain Reaction) method Materials and Methods HBeAg serum examination and HBV DNA quantification test were conducted on 270 hepatitis B patients undergoing anti-virus treatment after diagnosis of hepatitis B virus infection. Two serologic tests (IRMA, CMIA) with different detection principles were applied for the HBeAg serum test. Serum HBV DNA was quantitatively measured by real-time polymerase chain reaction (RT-PCR) using the Abbott m2000 System. Results The detection rate of HBeAg was 24.1% (65/270) for IRMA and 82.2% (222/270) for CMIA. Detection rate of serum HBV DNA by real-time RT-PCR is 29.3% (79/270). The measured amount of serum HBV DNA concentration is $4.8{\times}10^7{\pm}1.9{\times}10^8IU/mL$($mean{\pm}SD$). The minimum value is 16IU/mL, the maximum value is $1.0{\times}10^9IU/mL$, and the reference value for quantitative detection limit is 15IU/mL. The detection rates and concentrations of HBV DNA by group according to the results of HBeAg serological (IRMA, CMIA)tests were as follows. 1) Group I (IRMA negative, CMIA positive, N = 169), HBV DNA detection rate of 17.7% (30/169), $6.8{\times}10^5{\pm}1.9{\times}10^6IU/mL$ 2) Group II (IRMA positive, CMIA positive, N = 53), HBV DNA detection rate 62.3% (33/53), $1.1{\times}10^8{\pm}2.8{\times}10^8IU/mL$ 3) Group III (IRMA negative, CMIA negative, N = 36), HBV DNA detection rate 36.1% (13/36), $3.0{\times}10^5{\pm}1.1{\times}10^6IU/mL$ 4) Group IV(IRMA positive, CMIA negative, N = 12), HBV DNA detection rate 25% (3/12), $1.3{\times}10^3{\pm}1.1{\times}10^3IU/mL$ Conclusion HBeAg detection rate according to the serological test showed a large difference. This difference is considered for a number of reasons such as characteristics of the Ab used for assay kit and epitope, HBV of genotype. Detection rate and the concentration of the group-specific HBV DNA classified serologic results confirmed the high detection rate and the concentration in Group II (IRMA-positive, CMIA positive, N = 53).