• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.405-414
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• 2011

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules that are manually formulated by a language expert. These rules are appended each time a new error pattern is detected. However, such grammar checkers are not consistent. In order to resolve this shortcoming, we propose new method for generalizing error decision rules to detect the above errors. For this purpose, we use an existing thesaurus KorLex, which is the Korean version of Princeton WordNet. KorLex has hierarchical word senses for nouns, but does not contain any information about the relationships between cases in a sentence. Through the Tree Cut Model and the MDL(minimum description length) model based on information theory, we extract noun classes from KorLex and generalize error decision rules from these noun classes. In order to verify the accuracy of the new method in an experiment, we extracted nouns used as an object of the four predicates usually confused from a large corpus, and subsequently extracted noun classes from these nouns. We found that the number of error decision rules generalized from these noun classes has decreased to about 64.8%. In conclusion, the precision of our grammar checker exceeds that of conventional ones by 6.2%.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.397-406
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• 2008

Despite the effectiveness of oriental medical practice in the diagnosis of symptoms and providing cure to it, the preferences in western medicinal values is socially prevalent. The diagnosis of a disease using western medicinal practices provides us with an objective diagnostic result, however, decisions by oriental doctors are based on their heuristic intuitions developed by practice and experience. Objective solutions for the cure of symptoms using oriental medical therapy can have a high impact on the world market. Therefore, development of diagnostic machines based on oriental therapy can enhance the Ocular Inspection which is evaluated as one of the best diagnostic treatment among Oriental Medical Science, is not researched much compared to other diagnoses. Because there is no color diagnosis rules for digital machines to analyze the actual color, looking at the person's face color is one of the most important components to diagnose the disease or illness. The thesis proposes the implementation of absolute observing a person's face color standards of the color settings for objective diagnosis. As a results, comparative digital color analysis for observing a person's face color can be the most effective rule based Color scheme system to diagnose disease. A standard solution for the researching conditions is suggested to reduce the variable which may occur depending on the differences between the researching conditions.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.227-232
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• 2004

When we extract a representative term from text for information retrieval system or a special information for information retrieval and text milling system, we often need to solve the anaphora resolution problem. The antecedent decision problem of a pronoun is one of the major issues for anaphora resolution. In this paper, we are suggesting a method of deciding an antecedent of the third personal pronouns, such as “he/she/they” to analyze the contents of documents precisely. Generally, the antecedent of the third personal Pronouns seem to be the subject of the current statement or previous statement, and also it occasionally happens more than twice. Based on these characteristics, we have found rules for deciding an antecedent, by investigating a case of being an antecedent from the personal pronouns, which appears in the current statement and the previous statements. Since the heuristic rule differs on the case of the third personal pronouns, we described it as subjective case, objective case, and possessive case based on the case of the pronouns. We collected 300 sentences that include a pronoun from the newspaper articles on political issues. The result of our experiment shows that the recall and precision ratio on deciding the antecedent of the third personal pronouns are 79.0% and 86.8%, respectively.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.