• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.365-368
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• 2005

We propose a speech act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical form of a word, its part of speech (POS) tags, and bigrams of POS tags as sentence features and the contexts of the previous utterance as context features. We select informative features by Chi square statistics. After training SVM with the selected features, SVM classifiers determine the speech act of each utterance. In experiment, we acquired overall $90.54\%$ of accuracy with dialogue corpus for hotel reservation domain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2012-2030
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• 2021

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

• The Korean journal of helicobacter and upper gastrointestinal research
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• v.18 no.3
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• pp.186-197
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• 2018

Background/Aims: To investigate the predictive factors for improvement of atrophic gastritis (AG) and intestinal metaplasia (IM). Materials and Methods: A total of 778 subjects were prospectively enrolled and followed up for 10 years. Histological analysis of AG and IM was performed by using the updated Sydney system. To find the predictive factors for reversibility of AG and IM, 24 factors including genetic polymorphisms and bacterial and environmental factors were analyzed. Results: In all subjects, the predictive factor by multivariate analysis for improvement of both antral and corpus AG was successful eradication. The predictive factors for improvement of antral IM were age and successful eradication. The predictive factor for improvement of corpus IM was successful eradication. In patients with Helicobacter pylori infection, age and cagA were predictive factors for improvement of AG and IM. In patients with H. pylori eradication, monthly income and cagA were predictive factors for improvement of AG and IM. Conclusions: H. pylori eradication is an important predictive factor of regression of AG and IM and would be beneficial for the prevention of intestinal-type gastric cancer. Young age, high income, and cagA are additional predictive factors for improving AG and IM status. Thus, various factors affect the improvement of AG and IM.

• Investigative Magnetic Resonance Imaging
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• v.16 no.2
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• pp.115-123
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• 2012

Purpose : To evaluate white matter abnormalities on diffusion tensor imaging (DTI) in patients with mild Alzheimer disease (AD) and mild cognitive impairment (MCI), using tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM). Materials and Methods: DTI was performed in 21 patients with mild AD, in 13 with MCI and in 16 old healthy subjects. A fractional anisotropy (FA) map was generated for each participant and processed for voxel-based comparisons among the three groups using TBSS. For comparison, DTI data was processed using the VBM method, also. Results: TBSS showed that FA was significantly lower in the AD than in the old healthy group in the bilateral anterior and right posterior corona radiata, the posterior thalamic radiation, the right superior longitudinal fasciculus, the body of the corpus callosum, and the right precuneus gyrus. VBM identified additional areas of reduced FA, including both uncinates, the left parahippocampal white matter, and the right cingulum. There were no significant differences in FA between the AD and MCI groups, or between the MCI and old healthy groups. Conclusion: TBSS showed multifocal abnormalities in white matter integrity in patients with AD compared with old healthy group. VBM could detect more white matter lesions than TBSS, but with increased artifacts.

• Journal of Korean Neurosurgical Society
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• v.41 no.5
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• pp.283-290
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• 2007

Objective : This study was designed to analyze seizure outcome and to investigate the prognostic factors for predicting seizure outcome according to the preoperative evaluations, surgical procedures, topectomy sites and histopathological findings in patients with extratemporal lobe epilepsy [ETLE]. Methods : This study comprised 63 patients with ETLE who underwent surgery. Preoperative evaluations included semiologic analysis, chronic video-EEG monitoring, and neuroimaging studies. Surgical procedures consisted of topectomy in 51 patients, corpus callosotomy in 9, functional hemispherectomy in 2, and vagus nerve stimulation [VNS] in 1. Histopathological findings were reviewed. Postoperative seizure outcomes were assessed by Engel's classification at the average follow up period of 66.8 months. Chi-square test was used for statistics. Results : Total postoperative seizure outcomes were class I in 51 [80%] patients, class II in 6 [10%], class III in 6 [10%]. Patients with structural abnormalities on neuroimaging study showed class I in 49 [88%] patients [p<0.05]. Patients with focal and regional ictal EEG onset revealed class I in 47 [90%] patients [p<0.05]. Semiologic findings, surgical procedures, topectomy sites and histopathological findings did not show statistical correlation with seizure outcome [p<0.05]. Conclusion : A good seizure outcome was obtained in patients with ETLE. The factors for favorable seizure outcome are related to the presence of structural abnormalities on neuroimaging study, and focal and regional ictal EEG onset.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3911-3916
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• 2012

Background: The objective of this study WAS to describe cancer incidence rates and trends among THE Hong Kong population for the period 1983-2008. Methods: Incident cases and population data from 1983 to 2008 were obtained from the Hong Kong Cancer Registry and the Census and Statistics Department, respectively. Agestandardized incidence rates (ASIR) were estimated and joinpoint regression was applied to detect significant changes in cancer morbidity. Results: For all cancers combined, the ASIR showed declining trends (1.37% in men, 0.94% in women), this also being the case for cancers of lung, liver, nasopharynx, stomach, bladder, oesophagus for both genders and cervix cancer for women. With cancer of thyroid, prostate, male colorectal, corpus uteri, ovary and female breast cancer an increase was evident throughout the period. The incidence for leukemia showed a stable trend since early 1990s, following an earlier decrease. Conclusion: Although overall cancer incidence rates and certain cancers showed declining trends, incidence trends for colorectal, thyroid and sex-related cancers continue to rise. These trends in cancer morbidity can be used as an important resource to plan and develop effective programs aimed at the control and prevention of the spread of cancer amongst the Hong Kong population. It is particularly useful in allowing projection of future burdens on the society with the increase in certain cancer incidences.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7718-7728
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• 2015

Semantic similarity between words can be applied in many fields including computational linguistics, artificial intelligence, and information retrieval. In this paper, we present weighted method for measuring a semantic similarity between words in a document. This method uses edge distance and depth of WordNet. The method calculates a semantic similarity between words on the basis of document information. Document information uses word term frequencies(TF) and word concept frequencies(CF). Each word weight value is calculated by TF and CF in the document. The method includes the edge distance between words, the depth of subsumer, and the word weight in the document. We compared out scheme with the other method by experiments. As the result, the proposed method outperforms other similarity measures. In the document, the word weight value is calculated by the proposed method. Other methods which based simple shortest distance or depth had difficult to represent the information or merge informations. This paper considered shortest distance, depth and information of words in the document, and also improved the performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.131-143
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• 2014

We propose a no-reference perceptual fog density and visibility prediction model in a single foggy scene based on natural scene statistics (NSS) and perceptual "fog aware" statistical features. Unlike previous studies, the proposed model predicts fog density without multiple foggy images, without salient objects in a scene including lane markings or traffic signs, without supplementary geographical information using an onboard camera, and without training on human-rated judgments. The proposed fog density and visibility predictor makes use of only measurable deviations from statistical regularities observed in natural foggy and fog-free images. Perceptual "fog aware" statistical features are derived from a corpus of natural foggy and fog-free images by using a spatial NSS model and observed fog characteristics including low contrast, faint color, and shifted luminance. The proposed model not only predicts perceptual fog density for the entire image but also provides local fog density for each patch size. To evaluate the performance of the proposed model against human judgments regarding fog visibility, we executed a human subjective study using a variety of 100 foggy images. Results show that the predicted fog density of the model correlates well with human judgments. The proposed model is a new fog density assessment work based on human visual perceptions. We hope that the proposed model will provide fertile ground for future research not only to enhance the visibility of foggy scenes but also to accurately evaluate the performance of defog algorithms.