• Journal of Digital Convergence
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• v.11 no.3
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• pp.381-387
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• 2013

This study is the analysis of the form of the word appeared in the sixth grade of elementary school students' writing errors. The major findings of this study are as follows. 14532 words appeared, the average is 145.3. And 1903 sentences, and average 19.0 papers. On average, one sentence have consisted of 7.6 word. Second, the 69 people out of 100 had an error. This is serious. Because this study contains very basic contents. Third, the order of errors are abbreviations(33.09%), endings(27.70%), etc(19.78%), stems(19.42%). The results of this study can contribute to revise a elementary school textbooks. And the results of this study can contribute to select the contents of elementary spelling teaching.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.688-702
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• 1998

In this paper, we propose sign language morpheme generation rule corresponding to morpheme analysis for each part of speech. Korean natural sign language has extremely limited vocabulary, and the number of grammatical components eing currently used are limited, too. In this paper, therefore, we define natural sign language grammar corresponding to Korean language grammar in order to translate natural Korean language sentences to the corresponding sign language. Each phrase should define sign language morpheme generation grammar which is different from Korean language analysis grammar. Then, this grammar is applied to morpheme analysis/combination rule and sentence structure analysis rule. It will make us generate most natural sign language by definition of this grammar.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.438-440
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• 2014

Recently it is used commercially to actively move the data from the SNS service. Therefore, we propose a method that can accurately analyze the information related to the reputation of companies and products in real time SNS environment in this paper.Identify the relationship between words by performing morphological analysis on the text data gathered by crawling the SNS scheme. In addition, it shows the visualization to analyze statistically through a established emotional dictionary morphemes are extracted from the sentence. Here, if the extracted word is not exist in sentimental dictionary. Also, we propose the algorithm that add the word to emotional dictionary automatically.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.120-128
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• 2023

In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.

• Korean Journal of Social Welfare
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• v.67 no.2
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• pp.113-136
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• 2015

This study is about criminal judgement of child abuse. The purpose of this study is to analyze contents and reasons for judgements about child abuse crime in detail especially focused on elements and contents of weighing of an offense and concurrent imposition and then to suggest alternatives for policies and law-enforcement for the prevention of that crime. The data were collected from 484 written judgements on 579 criminal defendants that were related to 'Violation of the Child Welfare Act' and were conducted by content analysis. The results are as follows. First, Only about 25% criminal defendants were guilty of violations of the Child Welfare Act were sentenced to imprisonment and the rest of them(about 75%) were merely sentenced to probation of imprisonment or fined. Second, Proportion of prison sentence or period of jail time have not been increased in spite of public indignation and upward of statutory punishment by legislation. Third, in the case of child sexual abuse, there are frequent cases in which concurrent imposition was not put, regardless of explicit statement in the related laws. Last, this study revealed that some mitigation factors of sentence that have been identical to crimes against adult have been applied to child abuse crime uncritically, for example agreement and regret and so on. On the basis of such results, this study proposed policy alternatives for prevention of the recurrence of child abuse, i.e. intrinsic standard of weighing of an offense, concurrent imposition to perpetrators of child abuse and so on.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.

• Korean Journal of Digital Imaging in Medicine
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• v.13 no.1
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• pp.13-20
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• 2011

Now a days in our society, occupation boundaries have become blurred, and come into the limelight in the prior occupation or miss about new workplace. Medical area is no exception also, So we face urgent problem about protecting and spreading RT work-sphere simultaneously. This research allow to identify on RT role area of digital environment that is obscure profession-realm specially, and open up a new field hereafter. We examined present RT role area of digital environment in the more than thirty medical facility(general or university hospital) through questionnaire/visit survey from PACS administrator. Survey sentence comprises total 29 sentence over all main quadrisection-(eX. hospital formation & treatment state and PACS team composition & organization and PACS team workplace and PACS team daily workload), We performed comparative analysis in general contents perspectively. further more, divided main 5 section based on upper analysis and then manufactured output in consideration of each medical facility's operation state. There are comparative problem of hospital policy, So we maintained information security of each facility exhaustively. First, we separated a survey output into main 5 section as follows-(eX. PACS server & maintenance manage, Client/interlock manage, PACS data conversion, 3D reconstruction, PACS data im/export)-that received by 35 medical facility. And then manufactured output with comparative analysis about RT role area each section, general IT managing team about medical environment was out of existence that fill up with RT manpower in the surveyed medical facility consequently. What is worse, hospitals that entirely fill up with another worker were 3 place amazingly. Our specific statistic results show, the respondents was 63% that agree with reorganization of formation base on independence team, and supplement of the personnel average -continuous with upper agreement simply-was about 2.64. Further more, if reorganization break out with only RT manpower, quota TO will increase by geometric progression. Protecting and spreading role workplace is much accounted of the our inevitable project surely and more than 95% PACS administrator's have confidence in this proposition unconditionally. Henceforth, look forward to meeting the RT vision of many-sided multiplayer, based on acquire a specialized IT knowledge actively and open up a new work-field with frontier spirit.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.99-108
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• 2012

Many methods had been developed to improve the accuracy of extracting information from a vast amount of data. This paper combined a number of natural language processing methods such as NER (named entity recognition), sentence extraction, and part of speech tagging to carry out text analysis. The data source is comprised of texts obtained from the web using a domain-specific data extraction agent. A framework for the extraction of information from unstructured data was developed using the aforementioned natural language processing methods. We simulated the performance of our work in the extraction and analysis of texts for the detection of organizational structures. Simulation shows that our study outperformed other NER classifiers such as MUC and CoNLL on information extraction.

• Journal of Korean Academy of Nursing
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• v.44 no.6
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• pp.630-638
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• 2014

Purpose: The purpose of this study was to develop a scale to evaluate stress in elderly patients with coronary artery diseases (CAD) and to examine validity and reliability of the scale. Methods: The development process for the preliminary scale included construction of a conceptual framework and initial items, verification of content analysis, sentence correction, and pilot study. This study was conducted using a questionnaire survey with one-to-one interviews during January and February, 2012. Participants were 240 elderly patients with CAD. Data were analyzed using item analysis, factor analysis, criterion related validity, and internal consistency. Results: The developed scale consisted of 32 items and 6 factors - aging and disease (7 items), family relations (5 items), anxiety and withdrawal (9 items), management of daily living (3 items), compliance of medical regimen (4 items), poverty and finance (4 items), and explained 68.5% of total variance. The scale had significantly positive correlation with the Korean Perceived Stress Scale (KPSS). Cronbach's alpha was .96, and Guttman split half coefficient was .91. Conclusion: Results indicate that the Stress Scale for Elderly Patients with CAD has validity and reliability, and is a suitable scale in health care settings to assess stress in elderly patients with CAD.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.81-85
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• 2002

We present a statistical analysis of Korean phonological variations using automatic generation of phonetic transcription. We have constructed the automatic generation system of Korean pronunciation variants by applying rules modeling obligatory and optional phonemic changes and allophonic changes. These rules are derived from knowledge-based morphophonological analysis and government standard pronunciation rules. This system is optimized for continuous speech recognition by generating phonetic transcriptions for training and constructing a pronunciation dictionary for recognition. In this paper, we describe Korean phonological variations by analyzing the statistics of phonemic change rule applications for the 60,000 sentences in the Samsung PBS(Phonetic Balanced Sentence) Speech DB. Our results show that the most frequently happening obligatory phonemic variations are in the order of liaison, tensification, aspirationalization, and nasalization of obstruent, and that the most frequently happening optional phonemic variations are in the order of initial consonant h-deletion, insertion of final consonant with the same place of articulation as the next consonants, and deletion of final consonant with the same place of articulation as the next consonants. These statistics can be used for improving the performance of speech recognition systems.