• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.865-872
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• 2013

Two-level morphology analysis method is one of rule-based morphological analysis method. This approach handles morphological transformation using rules and analyzes words with morpheme connection information in a lexicon. It is independent of language and Korean Two-level system was also developed. But, it was limited in practical use, because of using very small set of lexicon built manually. And it has also a over-generation problem. In this paper, we propose an automatic construction method of Korean Two-level lexicon for PC-KIMMO from morpheme tagged corpus. We also propose a method to solve over-generation problem using lexical information and sub-tags. The experiment showed that the proposed method reduced over-generation by 68% compared with the previous method, and the performance increased from 39% to 65% in f-measure.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.128-136
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• 2016

It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.

• Annual Conference on Human and Language Technology
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• 1999.10d
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• pp.92-98
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• 1999

본 논문에서는 연세대학교 컴퓨터과학과에서 연구되어 온 형태소 분석 시스템에 대해 설명한다. 연세대학교 자연 언어 처리 시스템의 기본적인 바탕은 무엇보다도 대량의 말뭉치를 기반으로 하고 있다는 점이다. 예컨대, 형태소 분석 사전은 말뭉치 처리에 의해 재구성 되었으며, 3000만 어절로부터 추출되어 수작업에 의해 다듬어진 어휘 데이터베이스는 형태소 분석 결과의 상당 부분을 제한하여 일차적인 중의성 해결의 역할을 담당한다. 또한 복합어 분석 역시 말뭉치에서 얻어진 사전을 바탕으로 이루어진다. 품사 태깅은 bigram hmm에 기반하고 있으며 어휘 규칙 등에 의한 후처리가 보강되어 있다. 이렇게 구성된 형태소 분석기 및 품사 태거는 구문 분석기와 함께 연결되어 이용되고 있다.

• Journal of Korean language education
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• v.29 no.2
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• pp.1-23
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• 2018

This study aims to analyze words and phrases which are frequently used in newsletters from Korean elementary schools. In order to achieve this goal, high frequent words from school newsletters were selected and classified into content and function words, and the domains of the words were looked up. For this study 1,000 school newsletters were collected in the areas of Busan, Ulsan and Gyeongnam. In terms of parts of speech, nouns, especially common nouns, most frequently appeared in the school newsletters followed by verbs and adjectives. This result shows that for immigrant women who have basic knowledge on Korean language, it is useful to give translated words to get the message of school letters. Furthermore, school related terms such as facilities, regulations and activities of school and Chinese-based vocabularies are found in school newsletters. In case of verbs, the words which contain the meaning of requests and suggestions are used the most. Adjectives which are related to positive value and evaluation, and describing weather and season is frequently used as well.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.53-65
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• 2009

The part-of-speech determination is necessary for resolving the part-of-speech ambiguity in English-Korean machine translation. The part-of-speech ambiguity causes high parsing complexity and makes the accurate translation difficult. In order to solve the problem, the resolution of the part-of-speech ambiguity must be performed after the lexical analysis and before the parsing. This paper proposes the CatAmRes model, which resolves the part-of-speech ambiguity, and compares the performance with that of other part-of-speech tagging methods. CatAmRes model determines the part-of-speech using the probability distribution from Bayesian network training and the statistical information, which are based on the Penn Treebank corpus. The proposed CatAmRes model consists of Calculator and POSDeterminer. Calculator calculates the degree of appropriateness of the partof-speech, and POSDeterminer determines the part-of-speech of the word based on the calculated values. In the experiment, we measure the performance using sentences from WSJ, Brown, IBM corpus.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.292-297
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• 2001

In writing learning as a second/foreign language, a learner has to acquire not only lexical and syntactical knowledge but also the skills to choose suitable words for content which s/he is interested in. A learning system should extrapolate learner\\`s intention and give example phrases that concern with the content in order to support this on the system. However, a learner cannot always represent a content of his/her desired phrase as inputs to the system. Therefore, the system should be equipped with a diagnosis function for learner\\`s intention. Additionally, a system also should be equipped with an analysis function to score similarity between learner\\`s intention and phrases which is stored in the system on both syntactic and idiomatic level in order to present appropriate example phrases to a learner. In this paper, we propose architecture of an interactive support method for English writing learning which is based an analogical search technique of sample phrases from corpora. Our system can show a candidate of variation/next phrases to write and an analogous sentence that a learner wants to represents from corpora.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.385-395
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• 2005

Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.153-172
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• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.247-271
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• 2019

Uncertainty in scientific knowledge means an uncertain state where propositions are neither true or false at present. The existing studies have analyzed the propositions written in the academic literature, and have conducted the performance evaluation based on the rule based and machine learning based approaches by using the corpus. Although they recognized that the importance of word construction, there are insufficient attempts to expand the word by analyzing the meaning of uncertainty words. On the other hand, studies for analyzing the structure of networks by using bibliometrics and text mining techniques are widely used as methods for understanding intellectual structure and relationship in various disciplines. Therefore, in this study, semantic relations were analyzed by applying Word2Vec to existing uncertainty words. In addition, WordNet, which is an English vocabulary database and thesaurus, was applied to perform a network analysis based on hypernyms, hyponyms, and synonyms relations linked to uncertainty words. The semantic and lexical relationships of uncertainty words were structurally identified. As a result, we identified the possibility of automatically expanding uncertainty words.

• 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.