• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.78-80
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• 2021

Currently, research on natural language processing (NLP) is rapidly evolving. Natural language processing is a technology that allows computers to analyze the meanings of languages used in everyday life, and is used in various fields such as speech recognition, spelling tests, and text classification. Currently, the most commonly used natural language processing library is NLTK based on English, which has a disadvantage in Korean language processing. Therefore, after introducing KonLPy and Soynlp, the Korean Tokenizing libraries, we will analyze morphology analysis and processing techniques, compare and analyze modules with Soynlp that complement KonLPy's shortcomings, and use them as natural language processing models.

• The Journal of Information Systems
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• v.28 no.4
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• pp.155-174
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• 2019

Purpose Social interest in financial statement notes has recently increased. However, contrary to the keen interest in financial statement notes, there is no morphological analyzer for accounting terms, which is why researchers are having considerable difficulty in carrying out research. In this study, we build a morphological analyzer for accounting related text mining techniques. This morphological analyzer can handle accounting terms like financial statements and we expect it to serve as a springboard for growth in the text mining research field. Design/methodology/approach In this study, we build customized korean morphological analyzer to extract proper accounting terms. First, we collect Company's Financial Statement notes, financial information data published by KPFIS(Korea Public Finance Information Service), K-IFRS accounting terms data. Second, we cleaning and tokeninzing and removing stopwords. Third, we customize morphological analyzer using n-gram methodology. Findings Existing morphological analyzer cannot extract accounting terms because it split accounting terms to many nouns. In this study, the new customized morphological analyzer can detect more appropriate accounting terms comparing to the existing morphological analyzer. We found that accounting words that were not detected by existing morphological analyzers were detected in new customized morphological analyzers.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.89-97
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• 2019

This study proposes a method for investigating current patents related to information communication technology and smart mobility to provide insights into future technology trends. The method is based on text mining clustering analysis. The method consists of two stages, which are data preparation and clustering analysis, respectively. In the first stage, tokenizing, filtering, stemming, and feature selection are implemented to transform the data into a usable format (structured data) and to extract useful information for the next stage. In the second stage, the structured data is partitioned into groups. The K-medoids algorithm is selected over the K-means algorithm for this analysis owing to its advantages in dealing with noise and outliers. The results of the analysis indicate that most current patents focus mainly on smart connectivity and smart guide systems, which play a major role in the development of smart mobility.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.55-63
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• 2017

One of the purposes of Information Technology (IT) is to support human response to natural and social problems such as natural disasters and spread of disease, and to improve the quality of human life. Recent climate change has happened worldwide, natural disasters threaten the quality of life, and human safety is no longer guaranteed. IT must be able to support tasks related to disaster response, and more importantly, it should be used to predict and minimize future damage. In South Korea, the data related to the damage is checked out by each local government and then federal government aggregates it. This data is included in disaster reports that the federal government discloses by disaster case, but it is difficult to obtain raw data of the damage even for research purposes. In order to obtain data, information extraction may be applied to disaster reports. In the field of information extraction, most of the extraction targets are web documents, commercial reports, SNS text, and so on. There is little research on information extraction for government disaster reports. They are mostly text, but the structure of each sentence is very different from that of news articles and commercial reports. The features of the government disaster report should be carefully considered. In this paper, information extraction method for South Korea government reports in the word format is presented. This method is based on patterns and dictionaries and provides some additional ideas for tokenizing the damage representation of the text. The experiment result is F1 score of 80.2 on the test set. This is close to cutting-edge information extraction performance before applying the recent deep learning algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.4
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• pp.215-221
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• 2011

Mobile phones, which were used for simply calling and sending text messages, have recently moved to application-oriented digital devices such as smart phones and tablet phones. The rapid increase of smart and tablet phones which can offer advanced ability and run a variety of applications based on Java requires various digital multimedia content activities. These days, there are more than 2.2 billions of Christians around the world. Among them, more than 300 millions of people live in Asian, and all of them have and read the bible. If there is an application for the bible which translates from English to their own languages, it could be very helpful. With this reason, this paper proposes a multi-bible application that supports various languages. To do this, we implemented an algorithm that recognize sentences in the bible as word by word. The algorithm is essentially composed of the following three functions: tokenizing sentences in the bible into word by word (word tokenization), recognizing words by using touch event (word recognition), and translating the selected words to the desired language. Consequently, the proposed multi-bible application supports language translation efficiently by touching words of sentences in the bible.