• Title/Summary/Keyword: Predicted power

Search Result 1,302, Processing Time 0.019 seconds

The Validation Study of the Questionnaire for Sasang Constitution Classification (the 2nd edition revised in 1995) - In the field of profile analysis (사상체질분류검사지(四象體質分類檢査紙)(QSCC)II에 대(對)한 타당화(妥當化) 연구(硏究) -각(各) 체질집단(體質集團)의 군집별(群集別) Profile 분석(分析)을 중심(中心)으로-)

  • Lee, Jung-Chan;Go, Byeong-Hui;Song, Il-Byeong
    • Journal of Sasang Constitutional Medicine
    • /
    • v.8 no.1
    • /
    • pp.247-294
    • /
    • 1996
  • By means of the statistical data which has been collected with newly revised QSCC made use of the outpatient group examined at Kyung-Hee Medical Center and an open ordinary person group, the author proceeded statistical analysis for the validation study of the revised questionnaire itself. First, check the accurate discrimination rate by performing discriminant analysis on the statistical data of the patient group. And next, sought T-score by applying the norms gained in process of standadization of the open ordinary person group to the Sasang scale score of the outpatient group and investigated the distinctive feature between the subpopulations which was devided in the process of multivarite cluster analysis. The result was summarized as follows ; 1. The validity of the questionnaire was established through the fact that the accurate discrimination rate the ratio between predicted group and actual group was figured out 70.08%. 2. At the profile analysis the response to the relevant scale showed notable upward tendency in each constitutional group and therefore it seems to be pertinent in the field of constitutional discrimination. 3. In the observation of the power of expression through the profile analysis of each constitutional group the Soyang group demonstrated the most remarkable outcome, the Soeum group was the most inferior and the Taieum group revealed a sort of dual property. 4. What is called the group of seceder out of three subpopulation of each constitutional group distinguished definitely from the contrasted groups at the point of the distinctive profile feature and the content is like following description. (1) The seceder group of Soyang-in showed considerably passive disposition differently from general character of ordinary Soyang group and an appearance attracting the attention is that they demonstrated comparatively higher response at Soeum scale (2) The seceder group of Taieum-in gained low scores in general that informed the passive disposition of the group and the other way of the general property of Taieum group which showed accompanied ascension in Taiyang-Taieum scales they demonstrated sharply declined score at Taiyang scale (3) The seceder group of Soeum-in demonstrated distinctive property similar to the profile feature of Soyang group and it notifies that the passive property of Soeum group was diluted for the most part. According to the above result, the validity of newly revised questionnaire has been proven successfully and the property of seceder groups could be noticed to some degree through the profile analysis on the course of this study. The result of this study is expected to use as a research materials to produce next edition of the questionnaire and it is regarded that further inquisition about the difference between the seceder group and the contrasted group is required for the promotion of the questionnaire as it refered several times in the contents of the main discourse.

  • PDF

A Study on Knowledge Entity Extraction Method for Individual Stocks Based on Neural Tensor Network (뉴럴 텐서 네트워크 기반 주식 개별종목 지식개체명 추출 방법에 관한 연구)

  • Yang, Yunseok;Lee, Hyun Jun;Oh, Kyong Joo
    • Journal of Intelligence and Information Systems
    • /
    • v.25 no.2
    • /
    • pp.25-38
    • /
    • 2019
  • Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.