• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.58-74
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• 2018

This study aims to propose baseline data for designing restoration of Gaobyulup, researching space formation and characteristics of gardens of Gaobyulup, which located in the foot of Cheonmasan Mountain in Namyangju. Gaobyulup is a remain in retirement of Gyulsan Yu-Won Lee, a representative politician, administrator, and tea drinker in late Joseon Dynasty. The results of the research about the shape of Gaobyulup deducted through reference review, poetry and prose analysis, an on-the-spot survey and residents' interview are below: Lee, who used pseudonym as 'Gyulsan,' which menas Jongnamsan Mountain, yearned Mangcheonbyeoreop(輞川別業) by Yu Wang and retirement with a country house operation by Seogye Sedang Park. In the persuit of this ideal, he created and operated a country house in Gaogok of Yangju, which a family burial ground was located. Gaobyulup, which located in Gaogok in the lower part of Cheonmasan Mountain, was largely composed outer and inner gardens, and the area of house operation was started from a stone post of Gaobokji The inner garden of Gaobyulup was including major garden components like buildings, such as Sasihyanggwan, Obaekganjung, Imharyoe and Toesadam, and Chaewon near Haengrangchae, and Gwawon in an backyard. In addition, Younggwijung pavilion, which located 850m away from Gaobyulup, was the another country house inside the Byulup, thus Gaobyulup shows a duplex space formation. In the inner garden of Gaobyulup, there are Sasihyanggwan, which had functions of Sarangchae as library and depository of old paintings and calligraphic works, and Obaekganjung, a small Sarangchae which connected with Sasihyanggwan in the form of a transept. Yusanggoksuger located near Obaekganjung. Additionally, Imharyeo, a library with a tablet of Byeokryowon(??園), which located in the highest point in Byulup, has the functions of a reading room and a tea house. Many Taihu stones were located not only in Toesadam, a square-formed pond with lotus but also many places in the inner gardens. And rare garden plants were planted. These were closely related to the trend of horticulture for pleasure, wealth, and collecting old paintings and calligraphic works for pleasure of Lee. Meanwhile, the area of Younggwijung pavilion, located in Gaocheon stream fall from Byulup to Manhoiam, looks like Wooampok, a enjoying place of other personages, who use their pseudonym as "Oksan" or "Wooam" Lee identifies Wooampok as "Jesampok" and carved 'Gyulsan' s he declared this place is his operating area. Lee built Younggwijung pavilion and planted many peach trees for recreation of utopia. The stone letters of Byukpadongcheon, located in front of a bridge in the foreside of Younggwijung pavilion, seems another enchanted land created in Gaobokji inside. Lee carved Jeilsan in huge rock on the falls rear Manhoiam temple, which Lee did great role of foundation of the temple, so he identifies that this place was the end of the outer garden of Gaobyulup. This study tries to estimate traces of the country house in Gaogok through reference review and on-th-spot survey, and the results from this study are presumed based on site remains only conformed today. It needs to discover second scenary or stone carved letters between Jeilsan and Jesampok. Additionally, exact formation characteristics of Gaobyulup should be identified through excavation survey later. To do so, an interest and a major role of Namyangju-si must be equipped for future restoration of Gaobyulup.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Applied Biological Chemistry
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• v.17 no.3
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• pp.149-176
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• 1974

Eight substituted diphenyl ether herbicides and some of their photoproducts were studied in terms of solution phase photolysis under simulated environmental conditions by using a Rayonet photochemical reactor. The test compounds absorbed sufficient light energy at the wavelength of 300 nm to undergo various photoreactions. All the photoproducts were confirmed by means of tlc, glc, ir, ms, and/or nmr spectrometry. The results obtained are summarized as follows: Solution phase photolysis of C-6989: An exceedingly large amount of p-nitrophenol formed strongly indicates the readiness of the ether linkage cleavage of this compound as the main reaction in all solvents used. Photoreduction of nitro to amino group(s) and photooxidation of trifluoromethyl to carboxyl group were recognized as minor reactions. Aqueous photolysis of p-nitrophenol: Quinone(0.28%), hydroquinone (0.66%), and p-aminophenol (0.42%) were confirmed as photoproducts, in addition to a relatively small amount of an unknown compound. The mechanisms of formation of these products were proposed to be the nitro-nitrite rearrangement via $n{\rightarrow}{\pi}^*$ excitation and the photoreduction through hydrogen abstractions by radicals, respectively. Solution phase photolysis of Nitrofen: Photochemical reduction leading to the p-amino derivative was the main reaction in n-hexane. In aqueous solution, the photoreduction of nitro to amino group and hydroxylation predominated over the ether linkage cleavage. Nucleophilic displacement of the nitro group by hydroxide ion and replacement of chlorine substituents by hydroxyl group or, to a lesser extent, hydrogen were also observed as minor reactoins. Solution phase photolysis of MO-338: Photoreduction of the nitro to amino group was marked in the n-hexane solution photolysis. In the aqueous solution, photoreduction of the nitro substituent and hydroxylation were the main reactions with replacement of chlorine substituents by the hydroxyl group and hydrogen, and cleavage of the ether linkage as minor reactions. Photolyses of MC-4379, MC-3761, MC-5127, MC-6063, and MC-7181 in n-hexane and cyclohexane: Photoreduction of the nitro group leading to the corresponding amino derivative and replacement of one of the halogen substituents by hydrogen from the solvent used were the key reactions in each compound. Aqueous photolysis of MC-4379: Cleavage of the ether linkage, replacement of the carboxymethyl by hydroxyl group, hydroxylation, and replacement of the nitro by hydroxy group were prominent with photoreduction and dechlorination as minor reactions. Aqueous photolysis of MC-3761: Cleavage of the ether linkage, replacement of the carboxymethyl by hydroxyl group, and photoreduction followed by hydroxylation were the main reactions. Aqueous photolysis of MC-5127: Replacement of carboxyethyl by hydrogen was predominant with ether linkage cleavage, photoreduction, and dechlorination as minor reactions. It was obvious that the decarboxyethylation proceeded more readily than decarboxymethylation occurring in the other compounds. Aqueous photolysis of MC-6063: Cleavage of the ether linkage and photodechlorination were the main reactions. Aqueous photolysis of MC-7181: Replacement of the carboxymethyl group by hydrogen and monodechlorination were the remarkable reactions. Cleavage of the ether linkage and hydroxylation were thought to be the minor reactions. Aqueous photolysis of 3-carboxymethyl-4-nitrophenol: The photo-induced Fries rearrangement common to aromatic esters did not appear to occur in the carboxymethyl group of this type of compound. Conversion of nitro to nitroso group was the main reaction.

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

• Journal of Food Hygiene and Safety
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• v.21 no.4
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• pp.231-237
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• 2006

Nowadays the teabag is worldwide used for various products including green tea, tea, coffee, etc. since it is convenient for use. In case of outer packaging printed, however, there is a possibility that the plasticizers which is used for improvement in adhesiveness of printing ink may shift to inner tea bag. In this study, in order to monitor residual levels of plasticizers in teabags, we have established the simultaneous analysis method of 9 phthalates and 7 adipates plasticizers using gas chromatography (GC). These compounds were also confirmed using gas chromatography-mass spectrometry (GC-MSD). The recoveries of plasticizers analyzed by GC ranged from 82.7% to 104.6% with coefficient of variation of $0.6\sim2.7%$ and the correlation coefficients of each plasticizer was $0.9991\sim0.9999$. Therefore this simultaneous analysis method was showed excellent reproducibility and linearity. And limit of detection (LOD) and limit of quantitation (LOQ) on individual plasticizer were $0.1\sim3.5\;ppm\;and\;0.3\sim11.5\;ppm$ respectively. When 143 commercial products of teabag were monitored, no plasticizers analysed were detected in filter of teabag products. The migration into $95^{\circ}C$ water as food was also examined and the 16 plasticizers are not detected. In addition we carried out analysis of heavy metals, lead (Pb), cadmium (Cd), arsenic (As) and aluminum (Al) in teabag filters using ICP/AES. $Trace\sim23{\mu}g$ Pb per teabag and $0.6\sim1718{\mu}g$ Al per teabag were detected in materials of samples and Cd and As are detected less than LOQ (0.05 ppm). The migration levels of Pb and Al from teabag filter to $95^{\circ}C$ water were upto $11.5{\mu}g\;and\;20.8{\mu}g$ per teabag, respectively and Cd and As were not detected in exudate water of all samples. Collectively, these results suggest that there is no safety concern from using teabag filter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.3856-3868
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• 2011

Until recently, research trend in real estate has been focused on real estate market and the market analysis. But the studies on real estate training program development for real estate agents to improve their job performance are relatively short in numbers. Thus, this study shows empirical analysis of the needs for the training programs for real estate agents in Cheonan to improve their job performance. The results are as follows. First, in the survey of asking what educational contents they need in order to improve real estate agents' job performance, most of the respondents show their needs for the analysis of house's value, legal knowledge, real estate management, accounting, real estate marketing, and understanding of the real estate policy. This is because they are well aware that the best way of responding to the changing clients' needs comes from training programs. Secondly, asked about real estate marketing strategies, most of respondents showed their awareness of new strategies to meet the needs of clients. This is because new forms of marketing strategies including internet ads are needed in the field as the paradigm including Information Technology changes. Thirdly, asked about the need for real estate-related training programs, 92% of the respondents answered they need real estate education programs run by the continuing education centers of the universities. In addition, the survey showed their needs for retraining programs that utilize the resources in the local universities. Other than this, to have effective and efficient training programs, they demanded running a training system by utilizing the human resources of the universities under the name of the department of 'Real Estate Contract' for real estate agents' job performance. Fourthly, the survey revealed real estate management(44.2%) and real estate marketing(42.3%) is the most chosen contents they want to take in the regular course for improving real estate agents' job performance. This shows their will to understand clients' needs through the mind of real estate management and real estate marketing. The survey showed they prefer the training programs as an irregular course to those in the regular one. Despite the above results, this study chose subjects only in Cheanan and thus it needs to research more diverse areas. The needs of programs to improve real estate agents job performance should be analyzed empirically targeting the real estate agents not just in Cheonan but also cities like Pyeongchon, Ilsan and Bundang in which real estate business is booming, as well as undergraduate and graduate students whose major is real estate studies. These studies will be able to provide information to help develop the customized training programs by evaluating elements that real estate agents need in order to meet clients satisfaction and improve their job performance. Many variables of the program development learned through these studies can be incorporated in the curriculum of the real estate studies and used very practically as information for the development of the real estate studies in this fast changing era.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

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

• Korean Journal of Heritage: History & Science
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• v.46 no.1
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• pp.160-175
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• 2013

There is the need to make a sharp distinction as regards JANGSEUNGs (Korean traditional totem poles) that are different in origin, history and function. This study is to identify the functions of the figures, as well as to trace stone JANGSEUNGs to their origins. In this regard, researched were conducted into the origins of JANGSEUNGs and their changes in history. There was a tradition in the GORYEO Dynasty (an ancient dynasty in the Korean Peninsula) that it erected JANGSAENGs (the archaic name of JANGSEUNGs) or allied stone figures within temples; especially, 'TONGDOSA GUKJANGSAENG SEOKPYO (a stone JANGSAENG that was erected by the royal command and is at the entrance of TONGDO Temple located in YANGSAN, South GYEONGSANG Province, South Korea)' functions as a stone monument rather than as a stone sign. In the engraved inscription, it is written that it should be erected in the form of PANA as before. 'PANA' refers to 'ZHONGKUI', a god in Chinese Taoism believed to exorcise devils that spread diseases. The inscription is to define the territory of TONGDO Temple. The article on HAN JUN GYEOM in a book 'WORAKGI (a travelogue on WORAK Mountain in North CHUNGCHEONG Province, South Korea)' written by HEO MOK makes it possible to guess the scale of GUKJANGSAENGs erected in DOGAP Temple. The stones, on which 'GUKJANGSAENG' or 'HWANGJANGSAENG' were engraved, are not JANGSAENGs but are demarcation posts. In the JOSEON Dynasty (the last dynasty in the Korean Peninsula) JANGSAENGs functioned as signposts. Unlike JANGSAENGs in temples, they were made of wood. At first, the word 'JANGSAENG' was written '長生' in Chinese characters, but in the JOSEON Dynasty another character '木 (wood)' was added to them, and thus the orthography was likely to change into 'JANGSEUNG.' In the JOSEON Dynasty, in addition, optative or geomantic figures were not called 'JANGSEUNG.' Historically, for instance, there has been no case where 'DOL HARBANGs (stone figures found only in JEJU ISLAND, South Korea)' are called 'JANGSEUNG.' In a book 'TAMRA GINYEON (a historical record on JEJU Island, South Korea)' it is written that KIM MONG GYU, JEJU governor, erected ONGJUNG Stones outside the fortress gate. ONGJUNG Stones usually refer to stone statues erected in front of ancient kings or dignitaries' mausoleums. Moreover, they were geomantic figures erected to suppress miasma. A magazine 'GWANGJUEUPJI (a journal on old GWANGJU, South Korea, 1899)' shows that two two ONGJUNG Stones were so erected that they might look at each other to suppress miasma from a pathway through which lucks lose. On the two stone figures located in BUAN-EUP, North JEOLLA Province, South Korea, inscriptions 'SANGWON JUJANGGUN' and 'HAWON DANGJANGGUN' were engraved. The words are to identify the figures' sexes. They are a kind of optative geomantic figures, and therefore there is no reason to call them 'JANGSAENG' or 'JANGSEUNG' or 'DANGSAN.' The words 'SANGWON' and 'HAWON' are closely associated with Taoism. Since then, the words have been widely used as inscriptions on stone figures in temples, and subsequently are used for JANGSEUNGs. A hatted ONGJUNG Stone, found in BUKANSAN Fortress, disappeared and other ones may be being buried somewhere. Meanwhile, ONGJUNG Stones in JEJU Island and stone figures in BUAN-EUP have hardly been displaced and thus have properly functioned. Stone figures, made in those days, seem to be most similar in function to JANGSAENGs made during the GORYEO Dynasty. Specifically, like earlier JANGSAENGs, stone figures made during the early to mid-18th century were likely to function not only as optative figures but as boundary stones. Most of stone figures in temples were made whenever the land use survey was conducted throughout the nation, but given that at the same period of time, the commonalty filed many lawsuits against grave sites, temples might erect many stone figures to mark their territories. Currently, wooden or stone figures are commonly called 'JANGSEUNG', but they were erected in different epochs and for different reasons. Their origins are to be sought in stone figures that functioned not only as optative figures in temples but as boundary stones during the GORYEO Dynasty.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.161-177
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• 2019

In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.