• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.1-11
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• 2021

The purpose of this study was to analyze the trends and characteristics of 'stress urinary incontinence' research through word frequency analysis, and their relationships were modeled using word embedding. Abstract data of 9,868 papers containing abstracts in PubMed's MEDLINE were extracted using a Python program. Then, through frequency analysis, 10 keywords were selected according to the high frequency. The similarity of words related to keywords was analyzed by Word2Vec machine learning algorithm. The locations and distances of words were visualized using the t-SNE technique, and the groups were classified and analyzed. The number of studies related to stress urinary incontinence has increased rapidly since the 1980s. The keywords used most frequently in the abstract of the paper were 'woman', 'urethra', and 'surgery'. Through Word2Vec modeling, words such as 'female', 'urge', and 'symptom' were among the words that showed the highest relevance to the keywords in the study on stress urinary incontinence. In addition, through the t-SNE technique, keywords and related words could be classified into three groups focusing on symptoms, anatomical characteristics, and surgical interventions of stress urinary incontinence. This study is the first to examine trends in stress urinary incontinence-related studies using the keyword frequency analysis and word embedding of the abstract. The results of this study can be used as a basis for future researchers to select the subject and direction of the research field related to stress urinary incontinence.

• Journal of the Korean Society of Safety
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• v.38 no.3
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• pp.69-76
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• 2023

The purpose of the rules on the Occupational Safety and Health Standards (hereafter safety and health rules) is to regulate the safety and health measures stipulated in the Occupational Safety and Health Act and the specific instructions necessary for their implementation. However, the safety and health rules are extensive and complexly connected, making navigation difficult for users. In order for users to readily access safety and health rules, this study analyzed the frequency, distribution, and significance of terms included in the overall rules. First, the term distribution index was created based on the frequency and distribution of words extracted through text mining. The term distribution index derives from whether a word appears only in a specific chapter or across all rules. This allows users to effectively explore terms to be followed in a specific working environment and terms to be complied with in the overall working environment. Next, the related words of the previously derived terms were visualized through t-SNE and the Word2Vec algorithm. This can help prioritize the things that need to be managed first, focusing on key terms without checking the overall rules. Moreover, this study can help users explore safety and health rules by allowing them to understand the distribution of words and visualize related terms.

• Information Systems Review
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• v.23 no.4
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• pp.127-146
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• 2021

Fake news isexpanded and reproduced rapidly regardless of their authenticity by the characteristics of modern society, called the information age. Assuming that 1% of all news are fake news, the amount of economic costs is reported to about 30 trillion Korean won. This shows that the fake news isvery important social and economic issue. Therefore, this study aims to develop an automated detection model to quickly and accurately verify the authenticity of the news. To this end, this study crawled the news data whose authenticity is verified, and developed fake news prediction models using word embedding (Word2Vec, Fasttext) and deep learning algorithms (LSTM, BiLSTM). Experimental results show that the prediction model using BiLSTM with Word2Vec achieved the best accuracy of 84%.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.56-63
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• 2021

The narrative texts of industrial accident reports help to identify accident risk factors. They relate the accident triggers to the sequence of events and the outcomes of an accident. Particularly, a set of related keywords in the context of the narrative can represent how the accident proceeded. Previous studies on text analytics for structuring accident reports have been limited to extracting individual keywords without context. We proposed a context-based analysis using a Natural Language Processing (NLP) algorithm to remedy this shortcoming. This study aims to apply Word2Vec of the NLP algorithm to extract adjacent keywords, known as word embedding, conducted by the neural network algorithm based on supervised learning. During processing, Word2Vec is conducted by adjacent keywords in narrative texts as inputs to achieve its supervised learning; keyword weights emerge as the vectors representing the degree of neighboring among keywords. Similar keyword weights mean that the keywords are closely arranged within sentences in the narrative text. Consequently, a set of keywords that have similar weights presents similar accidents. We extracted ten accident processes containing related keywords and used them to understand the risk factors determining how an accident proceeds. This information helps identify how a checklist for an accident report should be structured.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.250-259
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• 2019

Big data processing technology and artificial intelligence (AI) are increasingly attracting attention. Natural language processing is an important research area of artificial intelligence. In this paper, we use Korean news articles to extract topic distributions in documents and word distribution vectors in topics through LDA-based Topic Modeling. Then, we use Word2vec to vector words, and generate a weight matrix to derive the relevance SCORE considering the semantic relationship between the words. We propose a way to recommend documents in order of high score.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.10-27
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• 2018

The purpose of this study is to explore the meaning of happiness and unhappiness in Korean society through text mining analysis. Similar words with keywords(happiness/unhappiness) from online news portal are extracted using Word2Vec and TF-IDF method. We also use the K-LIWC dictionary to perform the sentiment analysis of words associated with happiness and unhappiness. In TF-IDF analysis, happiness and unhappiness are highly related to social factors and social issues of the year. In Word2Vec analysis, 'Hope' has been similar with happiness for six years. In K-LIWC analysis, 'money/financial issues', 'school', 'communication' is highly related with happiness and unhappiness. In addition, 'physical condition and symptom' is highly related to unhappiness. Implications, limitations, and suggestions for future research are also discussed.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.129-142
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• 2018

Recently, the number of patent applications have been increasing rapidly every year as the importance of protecting intellectual property rights becomes more important. Patents must be inventive and have novelty. Especially, the novelty implies that the corresponding invention is not the same as the previous invention. To confirm the novelty, prior art search must be conducted before and after the application. The target of prior art search should include not only Korean patents but also foreign patents. Search of foreign patents should be supported by multilingual search techniques. However, a dictionary-based naive approach shows a limitation because some technical concepts are represented in different terms according to each nation. For example, a Korean term and a Japanese term may not be synonym even though they represent the same technical concept. In this paper, we propose a new method to map semantic similarity between technical terms in Korean patents and Japanese patents. To investigate different representations in each nation for the same technical concept, we identified and analyzed pairs of patents those are mutually connected with priority claim relationship. By performing an experiment with real-world data, we showed that our approach can reveal semantically similar technical terms in other language successfully.

• Journal of Biomedical Engineering Research
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• v.43 no.2
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• pp.109-115
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• 2022

Purpose: Medical records classification using vectorization techniques plays an important role in natural language processing. The purpose of this study was to investigate proper vectorization techniques for electronic medical records classification. Material and methods: 403 electronic medical documents were extracted retrospectively and classified using the cosine similarity calculated by Scikit-learn (Python module for machine learning) in Jupyter Notebook. Vectors for medical documents were produced by three different vectorization techniques (TF-IDF, latent sematic analysis and Word2Vec) and the classification precisions for three vectorization techniques were evaluated. The Kruskal-Wallis test was used to determine if there was a significant difference among three vectorization techniques. Results: 403 medical documents were relevant to 41 different diseases and the average number of documents per diagnosis was 9.83 (standard deviation=3.46). The classification precisions for three vectorization techniques were 0.78 (TF-IDF), 0.87 (LSA) and 0.79 (Word2Vec). There was a statistically significant difference among three vectorization techniques. Conclusions: The results suggest that removing irrelevant information (LSA) is more efficient vectorization technique than modifying weights of vectorization models (TF-IDF, Word2Vec) for medical documents classification.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.251-254
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• 2017

본 연구에서는 COPD와 기존에 연관이 있는 것으로 알려진 바이오마커 이외의 새로운 바이오마커를 찾고자 한다. Pubmed Data에서 선정한 암 관련 바이오마커를 추출하여 COPD와 암 관련 바이오마커의 관계를 파악하는 데이터로 사용한다. 그리고 워드 임베딩 모델 중 Word2vec을 사용하여 워드 임베딩 한다. 워드 임베딩한 K차원의 COPD와 암 관련 바이오마커를 t-SNE를 사용하여 시각화한다. 또한 코사인 유사도를 이용하여 COPD와 암 관련 바이오마커의 유사도를 측정한다. 그리고 코사인 유사도와 t-SNE 결과를 이용하여 COPD와 암 관련 바이오마커와의 상관관계를 파악할 수 있으며, 암 관련 바이오마커와 COPD 관련 바이오마커를 비교 하여 기존의 COPD와 연관이 있다고 알려진 바이오마커 이외의 새로운 바이오마커를 찾을 수 있다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.251-254
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• 2017

본 연구에서는 COPD와 기존에 연관이 있는 것으로 알려진 바이오마커 이외의 새로운 바이오마커를 찾고자 한다. Pubmed Data에서 선정한 암 관련 바이오마커를 추출하여 COPD와 암 관련 바이오마커의 관계를 파악하는 데이터로 사용한다. 그리고 워드 임베딩 모델 중 Word2vec을 사용하여 워드 임베딩 한다. 워드 임베딩한 K차원의 COPD와 암 관련 바이오마커를 t-SNE를 사용하여 시각화한다. 또한 코사인 유사도를 이용하여 COPD와 암 관련 바이오마커의 유사도를 측정한다. 그리고 코사인 유사도와 t-SNE 결과를 이용하여 COPD와 암 관련 바이오마커와의 상관관계를 파악할 수 있으며, 암 관련 바이오마커와 COPD 관련 바이오마커를 비교 하여 기존의 COPD와 연관이 있다고 알려진 바이오마커 이외의 새로운 바이오마커를 찾을 수 있다.