• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.555-567
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• 2019

Speech recognition rate is lowered even in a noisy environment, and it is difficult for a person with a speech disability or an inconvenient language to use it in a social life. In addition to improving the inconvenience of using the language, 280 words were selected using the word selection method which was improved when the word was selected considering the pronunciation characteristics of the language impaired. The MEMS development device used in the experiment was made considering material, lead wire type, length and direction. We improved the speech recognition rate by using the developed word selection method and the MEMS device developed to improve the speech recognition rate due to incorrect pronunciation and severe noise. The new method of selecting words and the mems device were improved and the results were included.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.2
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• pp.5-25
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• 2011

This study aims to identify the most effective statistical feature selecting method and context window size for word sense disambiguation using supervised methods. In this study, features were selected by four different methods: information gain, document frequency, chi-square, and relevancy. The result of weight comparison showed that identifying the most appropriate features could improve word sense disambiguation performance. Information gain was the highest. SVM classifier was not affected by feature selection and showed better performance in a larger feature set and context size. Naive Bayes classifier was the best performance on 10 percent of feature set size. kNN classifier on under 10 percent of feature set size. When feature selection methods are applied to word sense disambiguation, combinations of a small set of features and larger context window size, or a large set of features and small context windows size can make best performance improvements.

• Annual Conference on Human and Language Technology
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• 1993.10a
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• pp.271-283
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• 1993

본 논문은 온라인 한글인식 시스템에서 오인식된 단어를 교정하는 알고리즘이다. 교정 기법으로는 N-gram 한글사전을 이용하였다. 오인식된 단어는 후보키의 선정과 선정된 후보문자중 가장 유사한 단어로 대체된다. 오인식 단어는 사전에 수록된 단어의 형태소 정보 즉, 사전의 표제어, 이의 품사 및 접속 규칙을 활용하여 교정된다. 본 논문은 오인식 교정에서 필요한 한글의 형태소 분석기에 관한 선행연구를 전제한다.

• Proceedings of the Korean Society for Information Management Conference
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• 2004.08a
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• pp.163-166
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• 2004

텍스트 범주화에 관한 선행연구에서 자주 사용되면서 좋은 성능을 보인 자질 선정 기법은 문헌빈도와 카이제곱 통계량 등이다. 그러나 이들은 단어 자체가 갖고 있는 모호성은 제거하지 못한다는 단점이 있다. 본 연구에서는 kNN 분류기를 이용한 범주화 실험에서 단어간의 상호 관련성이 자동적으로 유도됨으로써 단어 자체 보다는 단어의 개념을 분석하는 잠재의미색인 기법을 자질 선정 방법으로 제안한다.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.723-734
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• 2004

In this paper, we present an automated Web page classification system based upon ontology. As a first step, to identify the representative terms given a set of classes, we compute the product of term frequency and document frequency. Secondly, the information gain of each term prioritizes it based on the possibility of classification. We compile a pair of the terms selected and a web page classification into rules using machine learning algorithms. The compiled rules classify any Web page into categories defined on a domain ontology. In the experiments, 78 terms out of 240 terms were identified as representative features given a set of Web pages. The resulting accuracy of the classification was, on the average, 83.52%.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2000.06a
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• pp.179-183
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• 2000

정보검색시스템은 사용자의 질의를 입력받아 사용자가 원하는 정보를 검색해주는 시스템을 의미한다. 그러나, 대부분의 정보검색시스템은 단어와 연산자의 조합으로 이루어진 질의를 입력받아 문서를 검색해 주고, 사용자는 그 문서들 중에서 원하는 정보를 다시 찾아내야 한다. 본 논문에서는 영어 자여어질의를 입력 받아 사용자가 원하는 정보에 좀 더 근접한 형태의 답으로서 제한된 길이의 짧은 답을 제시하는 시스템을 구현한다. 시스템은 크게 질의분석단계, 문서검색 및 분석단계, 정보추출단계의 세 단계로 나눌 수 있다. 사용자 질의분석단계에서는 의문사 정보와 오토마타, 시소러스 범주 정보를 이용하여 질의에 대한 정답이 될 수 있는 단어의 속성을 예측하였다. 문서분서단계에서는 정답이 될 수 있는 단어의 후보를 선정하기 위해서 시소러스의 범주정보를 사용하였고, 선정된 정답후보중에서 정답을 추출하기 위해 각 후보단어의 질의단어와의 평균거리가중치, 범주간유사도, 공기질의어비율을 사용하였다. 실험을 통해 평균거리가중치만을 이용하는 것 보다 범주간유사도와 공기질의어비율을 함께 이용하는 것이 성능의 향상을 보였다.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.747-754
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• 2004

In this paper, we obtain an optimal corpus that can represent its characteristics very well from the baseline corpus which consists of unique 1,566,943 names among company names in a directory assistance serve (114). Two kinds of optimal solutions ared considered to obtain the optimal corpus. The first solution is to find phonetically balanced corpus (PBC), which are the minimum set including all possible triphones in the baseline corpus. The second solution is to find the phonetically distributed corpus (PDC), which is a minimum set representing the frequency characteristics of triphones in the baseline corpus. We can obtain 8,699 words as the PBC and 16,783 words (similarity measure R = 0.92) as PDC, respectively. These corpora can be used for the development of speech recognition and speech synthesis.

• Annual Conference on Human and Language Technology
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• 2000.10d
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• pp.179-183
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• 2000

정보검색시스템은 사용자의 질의를 입력받아 사용자가 원하는 정보를 검색해주는 시스템을 의미한다. 그러나, 대부분의 정보검색시스템은 단어와 연산자의 조합으로 이루어진 질의를 입력받아 문서를 검색해 주고, 사용자는 그 문서들 중에서 원하는 정보를 다시 찾아내야 한다. 본 논문에서는 영어 자연어질의를 입력받아 사용자가 원하는 정보에 좀 더 근접한 형태의 답으로서 제한된 길이의 짧은 답을 제시하는 시스템을 구현한다. 시스템은 크게 질의분석단계, 문서검색 및 분석단계, 정보추출단계의 세 단계로 나눌 수 있다. 사용자 질의분석단계에서는 의문사 정보와 오토마타, 시소러스 범주정보를 이용하여 질의에 대한 정답이 될 수 있는 단어의 속성을 예측하였다. 문서분석단계에서는 정답이 될 수 있는 단어의 후보를 선정하기 위해서 시소러스의 범주정보를 사용하였고, 선정된 정답후보 중에서 정답을 추출하기 위해 각 후보단어의 질의어단어와의 평균거리가중치, 범주간유사도, 공기질의어비율을 사용하였다. 실험을 통해 평균거리가중치만을 이용하는 것 보다 범주간유사도와 공기질의어비율을 함께 이용한 것이 성능의 향상을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.665-668
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• 2004

최근의 웹 문서는 텍스트뿐만 아니라 이미지, 사운드 등 다른 여러 형태로 표현되고 있어서 텍스트의 비중이 낮아지고 있다. 그래서 문서 내에서 일정량 이상의 단어 추출이 어려운 문서들에 대해서 기존의 단어 정보만을 이용한 문서 범주화 방법은 좋은 성능을 기대할 수 없다. 그래서 본 논문은 Anchor Text 단어 정보의 자질 적합성 판단에 의한 새로운 자동 문서 범주화 모델을 제안한다. 문서 범주화 모델로는 베이지언 확률 모델을 이용하였으며, 카이제곱 통계량을 사용하여 자질을 선정하였다. 문서 내에서 추출된 단어 자질들이 해당 문서를 판단하는데 부족하다고 판단되면 문서의 링크정보를 이용하여 연결된 문서의 단어 자질과 Anchor Text의 단어 자질을 반영함으로써 성능을 향상시킨다.