• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.326-329
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• 2019

기계독해를 실용화하기 위해 단락을 검색하는 검색 모델은 최근 기계독해 모델이 우수한 성능을 보임에 따라 그 필요성이 더 부각되고 있다. 그러나 기존 검색 모델은 질의와 단락의 어휘 일치도나 유사도만을 계산하므로, 기계독해에 필요한 질의 어휘의 문맥에 해당하는 단락 검색을 하지 못하는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위해 Word2vec의 입력 단어열의 벡터에 해당하는 IN Weight Matrix와 출력 단어열의 벡터에 해당하는 OUT Weight Matrix를 사용한 단락 검색 모델을 제안한다. 제안 방법은 기존 검색 모델에 비해 정확도를 측정하는 Precision@k에서 좋은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2180-2197
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• 2019

With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.31-41
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• 2018

Text classification has been studied for a long time in the Natural Language Processing field. In this paper, we propose an article- and paragraph-level genre classification system using Word2Vec-based LSTM, GRU, and CNN models for large-scale English corpora. Both article- and paragraph-level classification performed best in accuracy with LSTM, which was followed by GRU and CNN in accuracy performance. Thus, it is to be confirmed that in evaluating the classification performance of LSTM, GRU, and CNN, the word sequential information for articles is better than the word feature extraction for paragraphs when the pre-trained Word2Vec-based word embeddings are used in both deep learning-based article- and paragraph-level classification tasks.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.117-128
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• 2017

Purpose: This study analyzes automobile quality review data to develop alternative analytical method of informal data. Existing methods to analyze informal data are based mainly on the frequency of informal data, however, this research tries to use correlation information of each informal data. Method: After sentimental analysis to acquire the user information for automobile products, three classification methods, that is, $na{\ddot{i}}ve$ Bayes, random forest, and support vector machine, were employed to accurately classify the informal user opinions with respect to automobile qualities. Additionally, Word2vec was applied to discover correlated information about informal data. Result: As applicative results of three classification methods, random forest method shows most effective results compared to the other classification methods. Word2vec method manages to discover closest relevant data with automobile components. Conclusion: The proposed method shows its effectiveness in terms of accuracy and sensitivity on the analysis of informal quality data, however, only two sentiments (positive or negative) can be categorized due to human errors. Further studies are required to derive more sentiments to accurately classify informal quality data. Word2vec method also shows comparative results to discover the relevance of components precisely.

• Smart Media Journal
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• v.9 no.3
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• pp.90-97
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• 2020

With the development of the Internet and smartphones, digital sound sources are easily accessible, and accordingly, interest in music search and recommendation is increasing. As a method of recommending music, research using melodies such as pitch, tempo, and beat to classify genres or emotions is being conducted. However, since lyrics are becoming one of the means of expressing human emotions in music, the role of the lyrics is increasing, so a study of emotion classification based on lyrics is needed. Therefore, in this thesis, we analyze the emotions of the farewell lyrics in order to subdivide the farewell emotions based on the lyrics. After constructing an emotion dictionary by vectoriziong the similarity between words appearing in the parting lyrics through Word2Vec learning, we propose a method of classifying parting lyrics emotions using Word2Vec and LSTM, which classify lyrics by similar emotions by learning lyrics using LSTM.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.620-629
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• 2019

An automated minutes summary system is required to objectively summarize and classify the contents of discussions or discussions for decision making. This paper designs and implements a minutes summary system using word2vec model to complement the existing minutes summary system. The proposed system is further implemented with word2vec model to remove index words during morpheme analysis and to extract representative sentences with common opinions from documents. The proposed system automatically classifies documents collected during the meeting process and extracts representative sentences representing the agenda among various opinions. The conference host can quickly identify and manage all the agendas discussed at the meeting through the proposal system. The proposed system analyzes various agendas of large-scale debates or discussions and summarizes sentences that can be representative opinions to support fast and accurate decision making.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.3-4
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• 2018

단어 의미 유사도 측정은 정보 검색이나 문서 분류와 같이 자연어 처리 분야 문제를 해결하는 데 큰 도움을 준다. 이러한 의미 유사도 측정 문제를 해결하기 위하여 단어의 계층 구조를 사용한 기존 연구들이 있지만 이는 단어의 의미를 고려하고 있지 않아 만족스럽지 못한 결과를 보여주고 있다. 본 논문에서는 국립국어원에서 간행한 표준국어대사전에 50만 어휘가 추가된 우리말샘 사전을 기반으로 하여 한국어 단어에 대한 계층 구조를 파악했다. 그리고 단어의 용례를 word2vec 모델에 학습하여 단어의 문맥적 의미를 파악하고, 단어의 정의문을 sent2vec 모델에 학습하여 단어의 사전적 의미를 파악했다. 또한, 구축된 계층 구조와 학습된 word2vec, sent2vec 모델을 이용하여 한국어 단어 의미 유사도를 측정하는 모델을 제안했다. 마지막으로 성능 평가를 통해 제안하는 모델이 기존 모델보다 향상된 성능을 보임을 입증했다.

• The Journal of Information Systems
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• v.30 no.1
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• pp.45-64
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• 2021

Purpose The traditional topic modeling technique makes it difficult to distinguish the semantic of topics because the key words assigned to each topic would be also assigned to other topics. This problem could become severe when the number of online reviews are small. In this paper, the extended model of topic modeling technique that can be used for analyzing a small amount of online reviews is proposed. Design/methodology/approach The extended model of being proposed in this paper is a form that combines the traditional topic modeling technique and the Word2Vec technique. The extended model only allocates main words to the extracted topics, but also generates discriminatory words between topics. In particular, Word2vec technique is applied in the process of extracting related words semantically for each discriminatory word. In the extended model, main words and discriminatory words with similar words semantically are used in the process of semantic classification and naming of extracted topics, so that the semantic classification and naming of topics can be more clearly performed. For case study, online reviews related with Udo in Tripadvisor web site were analyzed by applying the traditional topic modeling and the proposed extension model. In the process of semantic classification and naming of the extracted topics, the traditional topic modeling technique and the extended model were compared. Findings Since the extended model is a concept that utilizes additional information in the existing topic modeling information, it can be confirmed that it is more effective than the existing topic modeling in semantic division between topics and the process of assigning topic names.

• Journal of Information Technology Services
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• v.20 no.2
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• pp.77-90
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• 2021

Online and mobile games represent digital entertainment. Not only the game grows fast, but also it has been noted for unique business models such as a subscription revenue model and free-to-play with partial payment. But, a recent revenue mechanism, called a loot-box system, has been criticized due to overspending, weak protection to teenagers, and more over gambling-like features. Policy makers and research communities have counted on expert opinions, review boards, and temporal survey studies to build countermeasures to minimize negative effects of online and mobile games. In this process, speed was not seriously considered. In this study, we attempt to use a big data source to find a way of observing a trend for policy makers and researchers. Specifically, we tried to apply the Word2Vec data mining algorithm to news repositories. From the findings, we acknowledged that the suggested design would be effective in lightening issues timely and precisely. This study contributes to digital entertainment service communities by providing a practical method to follow up trends; thus, helping practitioners have concrete grounds for balancing public concerns and business purposes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.83-85
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• 2017

여러 약재의 복합적인 작용으로 치료를 행하는 한의학의 특성으로 여러 처방과 약재 조합들을 기억하고 있어야 하는 한의사의 어려움을 줄이고 환자에게 보다 높은 질의 의료 서비스를 제공할 수 있는 환경을 만드는 것이 목적이다. 다양하고 복합적인 약재의 조합으로 증상을 치료하는 한의학의 특성 때문에 셀 수 없이 많은 약재의 조합이 존재하며 한의사가 이 모든 조합을 기억하기는 어렵기 때문에 한의사들이 환자를 처방함에 있어 조금이라도 보탬이 될 수 있는 처방 지원 시스템을 개발할 필요가 있다. word2vec을 이용하여 처방과 약재의 조합을 추천해주며 분석을 통해 산출된 약재의 조합과 그 조합이 실제 의서에 존재하는지의 여부를 함께 알려주어 한의사가 보다 더 주의하여 환자에게 처방할 수 있다.