• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.135-158
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• 2003

The Purpose of this study is to investigate how information use contributes to learning. Conducted as part of a larger study, this study focuses on learning by analyzing students' use of cognitive skills during the Process of using information. Within the broad methodological framework of qualitative research in constructivist paradigm (Guba and Lincoln, 1998), the study applied the revised Bloom's taxonomy (Anderson and Krathwohl, 2000) as a particular framework to understand the Phenomenon. Participants included 21 high school juniors in an honors' class of persuasive speech. The study's combinational use of two techniques -concept mapping and individual interview - in a naturalistic setting Proved to be the unique methods for researching the reflection of information use in learning Products. The results revealed that changes in students' understanding occured in four types - simple, analytic, organizational, and holistic changes. The analysis using the revised Bloom's taxonomy showed that a variety of cognitive skills were used during the whole process of information use and that the use of higher levels of cognitive skills is particularly crucial.

• Phonetics and Speech Sciences
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• v.2 no.3
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• pp.101-113
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• 2010

The present study investigated how the kinematics of the /a/-to-/i/ tongue movement in Korean would be influenced by prosodic boundary. The /a/-to-/i/ sequence was used as 'transboundary' test materials which occurred across a prosodic boundary as in /ilnjəʃ$^h$a/ # / minsakwae/ ('일년차#민사과에' 'the first year worker' # 'dept. of civil affairs'). It also tested whether the V-to-V tongue movement would be further influenced by its syllable structure with /m/ which was placed either in the coda condition (/am#i/) or in the onset condition (/a#mi). Results of an EMA (Electromagnetic Articulagraphy) study showed that kinematical parameters such as the movement distance (displacement), the movement duration, and the movement velocity (speed) all varied as a function of the boundary strength, showing an articulatory strengthening pattern of a "larger, longer and faster" movement. Interestingly, however, the larger, longer and faster pattern associated with boundary marking in Korean has often been observed with stress (prominence) marking in English. It was proposed that language-specific prosodic systems induce different ways in which phonetics and prosody interact: Korean, as a language without lexical stress and pitch accent, has more degree of freedom to express prosodic strengthening, while languages such as English have constraints, so that some strengthening patterns are reserved for lexical stress. The V-to-V tongue movement was also found to be influenced by the intervening consonant /m/'s syllable affiliation, showing a more preboundary lengthening of the tongue movement when /m/ was part of the preboundary syllable (/am#i/). The results, together, show that the fine-grained phonetic details do not simply arise as low-level physical phenomena, but reflect higher-level linguistic structures, such as syllable and prosodic structures. It was also discussed how the boundary-induced kinematic patterns could be accounted for in terms of the task dynamic model and the theory of the prosodic gesture ($\pi$-gesture).

• Journal of Digital Convergence
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• v.17 no.2
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• pp.283-292
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• 2019

The loss of semantic knowledge and impairments in semantic associations by semantic category is gaining increasing attention, as indicators of early-stage cognitive decline. As such, we assigned semantic association task (SAT) to normal elderly (NE) and those with subjective memory impairment (SMI) or mild cognitive impairment (MCI) to examine their performance by semantic subcategories and the differences in error patterns. We found a significant difference in the number of correct response and reaction time under the SAT categories among the three groups, with the highest performance observed in 'function' and the lowest performance in 'superordinate' and 'part/whole'. Moreover, the error frequency was the lowest in NE, followed by those with SMI and MCI, with the latter two groups showing a significant increase in no-response. Our findings demonstrate the varying extent and process of impairments in the semantic network by category over different stages of cognitive decline. Thus, we proposed SAT performance as an indicator to detect and follow-up on cognitive decline in elderly with cognitive disorder.

• Phonetics and Speech Sciences
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• v.2 no.4
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• pp.165-171
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• 2010

Vocal process granuloma can occur commonly by laryngopharyngeal reflux (LPR), vocal abuse or misuse. It has been reported that voice therapy is employed with medication therapy for the patients who has vocal process granuloma, however research about effect of voice therapy can be hardly founded. For that matter, the primary aim of this study was to evaluate the effect of therapeutic method we implement. Thirty one patients who has been diagnosed with vocal process granuloma from January, 2007 to June, 2009 participated in this study. 19 patients among them are provided voice therapy and medication, 12 patients take only medication. Voice therapy is implemented ranging from 5 to 19 sessions (mean: 8.6 sessions). We provided explanation about problem each patient has, voice rest, SKMVTT$^{(R)}$, abdominal breathing, and relaxation in session. All subjects were examined by videostroboscopy, perceptual assessment, acoustic and aerodynamic measures. Consequantly, the greater part of the patients (78.9%) who is treated by voice therapy and medication are confirmed disappearance or decrease of granuloma, it shows better results compared with the group provided only medication (66.7%). Especially, the period of drug administration is 3.7 months in the group runs parallel with voice therapy, the period of other group is 7.8 months. The results of acoustic and aerodynamic measures after treating indicates there are significant decrease in Jitter, Shimmer, and NHR, and increase in MPT, Psub (p<.05). However, there is no large difference statistically even though voice quality has improved since the therapy. In conclusion, it is verified that the voice therapy to the vocal process granuloma patients taking medication is effectual method, we recommend combining voice therapy with medication when treatment is needed for the vocal process granuloma patients.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.909-915
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• 2008

Researches for time delay estimation had been studied broadly. However studies about factors for time delay estimation are insufficient, especially in case of real environment application. In 1997, Brandstein and Siverman announced that performance of time delay estimation deteriorates as reverberant time of room increases. Even though reverberant time of room is same, performance of estimation is different as the specific part of signals. In order to know that reason, we studied and analyzed the factors for time delay estimation using speech signal and room impulse response. In result, we can know that performance of time delay estimation is changed by different R/D ratio and signal characteristics in spite of same reverberant time.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.241-246
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• 2004

In this paper, we propose VCCV units as a processing unit of language model and compare them with clauses and morphemes of existing processing units. Clauses and morphemes have many vocabulary and high perplexity. But VCCV units have low perplexity because of the small lexicon and the limited vocabulary. The construction of language models needs an issue of the smoothing. The smoothing technique used to better estimate probabilities when there is an insufficient data to estimate probabilities accurately. This paper made a language model of morphemes, clauses and VCCV units and calculated their perplexity. The perplexity of VCCV units is lower than morphemes and clauses units. We constructed the N-grams of VCCV units with low perplexity and tested the language model using Katz, absolute, modified Kneser-Ney smoothing and so on. In the experiment results, the modified Kneser-Ney smoothing is tested proper smoothing technique for VCCV units.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.1-18
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• 2021

While content word-based frequency analysis has obvious limitations to intentional deception or irony, KLIWC has evolved into functional word analysis and KrKwic has evolved as a way to visualize co-occurrence frequencies. However, after more than 10 years of development, several issues still need improvement. Therefore, we tried to develop a new psychological language analysis program by analyzing KLIWC and KrKwic. First, the two programs were analyzed. In particular, the morpheme classification of KLIWC and the Korean morpheme analyzer was compared to enhance the functional word analysis function, and the psychological dictionary were analyzed to strengthen the psychological analysis. As a result of the analysis, the Hannanum part-of-speech analyzer was the most subdivided, but KLIWC for personal pronouns and KKMA for endings and endings were more subdivided, suggesting the integrated use of multiple part-of-speech analyzers to strengthen functional word analysis. Second, the research trends of studies that analyzed texts with these programs were analyzed. As a result of the analysis, the two programs were used in various academic fields, including the field of Interdisciplinary Studies. In particular, KrKwic was used a lot for the analysis of papers and reports, and KLIWC was used a lot for the comparative study of the writer's thoughts, emotions, and personality. Based on these results, the necessity and direction of development of a new psychological language analysis program were suggested.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.313-324
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• 2008

A sentence reduction is the information compression process which removes extraneous words and phrases and retains basic meaning of the original sentence. Most researches in the sentence reduction have required a large number of lexical and syntactic resources and focused on extracting or removing extraneous constituents such as words, phrases and clauses of the sentence via the complicated parsing process. However, these researches have some problems. First, the lexical resource which can be obtained in loaming data is very limited. Second, it is difficult to reduce the sentence to languages that have no method for reliable syntactic parsing because of an ambiguity and exceptional expression of the sentence. In order to solve these problems, we propose the sentence reduction method which uses templates and POS(part of speech) information without a parsing process. In our proposed method, we create a new sentence using both Sentence Reduction Templates that decide the reduction sentence form and Grammatical POS-based Reduction Rules that compose the grammatical sentence structure. In addition, We use Viterbi algorithms at HMM(Hidden Markov Models) to avoid the exponential calculation problem which occurs under applying to Sentence Reduction Templates. Finally, our experiments show that the proposed method achieves acceptable results in comparison to the previous sentence reduction methods.