• The Journal of Korean Medicine
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• v.16 no.2 s.30
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• pp.453-489
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• 1995

The research of the phrases related with physiology of stomach and spleen in the contents of Huang Di Nei Jing(黃帝內經) known as the Bible of oriental medicine will make a contribution to a deep understanding of disease of stomach and spleen and a proper clinical diagnosis and treatment of them. In this research of the most appropriate glosses recorded nine kinds of representative medical books including Huang Di Nei Jing Somoon(黃帝內經素問) of Wang Bing(王氷) were picked out: The summaries of the selected contents are as follows: 1. The word 'saliva(涎)' in 'the spleen controls saliva(脾爲涎)' can be viewed as a generic term referring to oral cavity secretion gland as well as the secretion fluid of salivary gland. 2. The phases 'a large reservoir(太倉)', barn organs', 'a reserboir of food stuff', 'a stomach as the market(胃爲之市)', etc mean the function of stomach to receive food(胃主受納). 3. The phase 'generation of five tastes(五味出焉)' means both 'the function of stomach to transform food into chyme(胃主腐熟)' and 'the channelling function of spleen.(脾主運化)' 4. The flowing of the food-Qi(食氣) into stomach brings about spreading Jung(精) into liver and then percolating Jung(精) flow into channel. The channel-Qi(脈氣) flows into lung through channel. As a result, all kinds of channels gather together in lung and Jung(精) is sent into skin and hair. The assembly of Jung(精) with skins and channels moves Qi(氣) into fu-organ and so jung(精) and mental activity(神明) in fu-organ(府) come to be in four organs(四臟). Then if Qi(氣) comes back to power balance unit(權衡) being in the state of equilibrium(權衡以平), the hole of Qi(氣口) comes to determine the matter of life and death through achieving Chun-quan-chi(-寸-關-尺). The above mentioned phrases means the digestion, asorption and transmission of food. When food is taken in stomach, Jung-Qi(精氣) comes to be over flowed upward into spleen, back into lung, finally downward into bladders through water-conduit(水道) controlled by lung. When water- Jung(水精) radiates into whole body with channels of five organs(五臟), both of them fit together with and yin-yang(陰-陽). Therefore, the grasping of the rise and decline of yin-yang(陰C-陽) is necessary to consult patients. The above mentioned phrases is properly viewed to designate the asorption, transmission and excretion of food. 5. Spleen controls flesh(脾之合肉也), the state of spleen is known by human lips, and what this means is that liver plays functions of spread and expansion(肝主疏泄). 6. The phrase 'Jung Jung'((中精)) in 'gallbladder dominates Jung jung(膽主中精)', which in one of the specific expression of 'liver plays functions of spread and expansion(肝主疏泄). 7. It is right that the phase 'The eleven organs in all are determined by gallbladder'(凡十,一臟取決於膽也) is correctly paraphrased as 'only one of ten organs, spleen, is determined by gallbladder'.(凡十,一臟取決於膽也), 8. The small intestine is an organ. which receives the materials digested and sends them out. This means that the function of transforming materials(化物) factually refers to that of separating clearity and blur(泌別淸濁). And it is also thought to have the function of ascending clearity and descending blur(升淸降濁), 9. A large intestine is a transmitting organ(傳導之官) from which a change comes out(變化出焉). the phrase 'change'(變化) in this sentence means both the intake of water and nutrition and the formation procedure of stool through excretion of mucocele.

• Sim-seong Yeon-gu
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• v.28 no.1
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• pp.50-87
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• 2013

C. G. Jung believed that universal and basic condition of human's Unconscious comes out from Märchen or mythology. We can easily experience these universality of human nature in dreams. Therefore, It is very important to interpret mythogens that appear in myths and märchen in analytical psychology to understand these 'big dreams' which could be seen in clinical practice. As I was interested in interpreting myths in analytic psychology, I tried to find universality of archetypes in Korea's traditional folk tales and took note of the birth myth of Hyeokgeose, the founder of Shilla dynasty, while examining the chater of the Unsual in history in the Heritage of the Three Kingdoms. Shilla was founded earlier than two other countries, but it was located in the very south of the Korean Peninsula, and it was behind times in politically, militarily, and culturally compare to Goguryeo and Baekje. However, Shilla achieved unifying the Three Kingdoms and it lasted 1000 years, the longest unified history in Korean history. I tried to examine archetypes in the birth myth if there are any backgrounds that are related to finding a Shilla Kingdom. It is noted that myth of the founder of Korean Peninsula's small Kingdom Shilla has complete story from before the birth to birth, birth of spouse, growth, marriage, accession, governing, death, after death, and succession. Symbols such as numbers 1, 3, 5, 6, 7, 13 and 61, various azimuthes including north, west, south, east, and central, animals like tiger, white horse, hen, dragon, phoenix, and snakes, natures like main symbol egg, rock, gourd, lightening, spring water, stream, tree, forest, mountain, iron and goddess-image like seon-do Holy Mother gradually appears in the myth. These symbols could show a meaning of human experience such as birth of Conscious, growth and development of paternal and maternal love, and story of regeneration and extinction. Moreover, It could be seen as these progress eternally continues in next generation. I have found out that a word, a sentence or stories that looks meaningless in myth revealed its true symbolical meaning. In addition, interaction between Unconscious and Conscious repeats in different forms, and expressed in layered.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.