• Language and Information
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• v.18 no.2
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• pp.1-29
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• 2014

Relativization is one of the common syntactic operations to merge two different clauses into a single information unit. This operation plays a pivotal role to structuralize multiple clauses cohesively as well as serves to specify the property an individual has within the context. That implies that relativization contributes to information structure of multiclausal sentences. In this context, this paper delves into information structure of relative clauses in English with an eye toward creation of a computational model from a standpoint of machine translation. The current work employs Head-driven Phrase Structure Grammar (HPSG, Pollard and Sag (1994)) as a theory of grammar and Minimal Recursion Semantics (MRS, Copestake et al. (2005) as a meaning representation system. Building upon these formalisms, this paper addresses how information structure of relative clauses can be represented and constrained. The current work makes use of Individual CONStraints (ICONS) for modeling relative clauses with respect to information structure. The current work also investigates which relative clause involves which information structure constraint. The present study argues that non-restrictive relative clauses impose a more specific constraint on information structure than restrictive relative clauses.

• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.591-601
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• 2009

In the present paper, one new generalized 'useful' information generating function and two new relative 'useful' information generating functions have been defined with their particular and limiting cases. It is interesting to note that differentiations of these information generating functions at t=0 or t=1 give some known and unknown generalized measures of useful information and 'useful' relative information. The information generating functions facilitates to compute various measures and that has been illustrated by applying these information generating functions for Uniform, Geometric and Exponential probability distributions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.625-631
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• 2011

This paper presents a discrete-time output feedback consensus algorithm for Multi-Agent Systems (MAS). Under the assumption that an agent is aware of the relative state information about its neighbors, a state feedback consensus algorithm is designed based on Linear Matrix Inequality (LMI) method. In general, however, it is possible to obtain its relative output information rather than the relative state information. To reconcile this problem, an Unknown Input Observer (UIO) is employed in this paper. To this end, first it is shown that the relative state information can be estimated using the UIO and the measured relative output information. Then a certainty-equivalence type output feedback consensus algorithm is proposed by combining the LMI-based state feedback consensus algorithm with the UIO. Finally, simulation results are given to illustrate that the proposed method successfully achieves the state consensus.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.218-223
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• 2010

Discussion and analysis about relative mutual information has been carried out through fuzzy entropy and similarity measure. Fuzzy relative mutual information measure (FRIM) plays an important part as a measure of information shared between two fuzzy pattern vectors. This FRIM is analyzed and explained through similarity measure between two fuzzy sets. Furthermore, comparison between two measures is also carried out.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.41-49
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• 2022

Documents often contain relative time expressions, and it is important to define a schema of the relative time information and develop a system that extracts such information from corpus. In this study, to deal with the relative time expressions, we propose seven additional attributes of timex3: year, month, day, week, hour, minute, and second. We propose a way to represent normalized values of the relative time expressions such as before, after, and count, and also design a set of rules to extract the relative time information from texts. With a new corpus constructed using the new attributes that consists of dialog, news, and history documents, we observed that our rule-set generally achieved 70% accuracy on the 1,041 documents. Especially, with the most frequently appeared attributes such as year, day, and week, we got higher accuracies compared to other attributes. The results of this study, our proposed timex3 attributes and the rule-set, will be useful in the development of services such as question-answer systems and chatbots.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1269-1279
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• 2008

Data mining is the technique to find useful information in huge databases. One of the well-studied problems in data mining is exploration for association rules. An association rule technique finds the relation among each items in massive volume databases by several interestingness measures. An important and useful classification scheme of interestingness measures may be based on user-involvement. This results in two categories - objective and subjective measures. This paper present some relative interestingess measures to compare with degrees of association for two groups. A comparative study with some relative interestingness measures is shown by numerical example. The results show that the relative net confidence is the best relative interestingness measure.

• Journal of Distribution Science
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• v.14 no.10
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• pp.165-178
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• 2016

Purpose - This study aims to empirically verify the impact of media richness, social presence, and prior life satisfaction on various continual usage or withdrawal behaviors of SNS users via both a positive path of satisfaction and a negative path of relative deprivation. By identifying these causal paths, we observe dynamic interactions of SNS user psychology in a balanced view, and provide some implications about design principles for SNS providers. Research design, data, and methodology - We developed 16 hypothesis based on media richness theory, social presence theory, social comparison theory, the literature about relative deprivation, and the literature about the various reactions of IS users. The rich SNS media, social presence recognition among peer SNS users, and prior life satisfaction could generate positive experience, attitude, and virtuous behavioral intentions among SNS users. At the same time, rich media, low social presence, and low prior life satisfaction could generate relative deprivation and could increase withdrawal behavioral intentions such as refusal to provide information, misrepresentation of information, and removal of uploaded information in SNS. Scenario surveys were conducted to collect data from potential SNS users. Data from 357 surveys were collected and analyzed through a PLS algorithm to test the hypotheses. Results - Media richness, social presence, and prior life satisfaction could significantly increase perceived enjoyment, satisfaction, and behavioral intention of continual usage and knowledge sharing. They also could significantly decrease refusal and misrepresentation intention. Relative deprivation is significantly decreased only by prior life satisfaction. Relative deprivation could not significantly decrease satisfaction, but it could significantly increase misrepresentation and removal intention, which could be regarded as information distortion intention. Conclusions - SNS providers should focus on developing rich media and social presence support because these two variables could impact the positive experiences of SNS users. Moreover, the positive experiences could heavily influence SNS user behavior. Some management is needed to prevent relative deprivation and its consequences of misrepresentation and removal intention. SNS providers should prevent SNS users from excessive image misrepresentation and removal as this information distortion could be the source of relative deprivation.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1345-1352
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• 2014

Data mining is the representative methodology to obtain meaningful information in the era of big data.By Wikipedia, association rule learning is a popular and well researched method for discovering interesting relationship between itemsets in large databases using association thresholds. It is intended to identify strong rules discovered in databases using different interestingness measures. Unlike general association rule, inverse association rule mining finds the rules that a special item does not occur if an item does not occur. If two types of association rule can be simultaneously considered, we can obtain the marketing information for some related products as well as the information of specific product marketing. In this paper, we propose a balanced attributable relative accuracy applicable to these association rule techniques, and then check the three conditions of interestingness measures by Piatetsky-Shapiro (1991). The comparative studies with rule accuracy, relative accuracy, attributable relative accuracy, and balanced attributable relative accuracy are shown by numerical example. The results show that balanced attributable relative accuracy is better than any other accuracy measures.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.813-820
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• 2009

In this paper, we review the utility-based generalization of the Shannon entropy and Kullback-Leibler information measure as the U-entropy and the U-relative entropy that was introduced by Friedman et al. (2007). Then, we derive some relations between the U-relative entropy and other information measures based on a parametric family of utility functions.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.347-353
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• 2004

We propose a simple nonparametric estimator of relative risk in the two sample case of the proportional hazards model for complete data. The asymptotic distribution of this estimator is derived using a functional equation. We obtain the asymptotic normality of the proposed estimator and compare with Begun's estimator by confidence interval through simulations.