• Korean Security Journal
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• no.1
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• pp.351-370
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• 1997

This study concerns possible measures to prevent separatists' terrorist acts against overseas Korean businessmen. Of late, many Korean enterprises are helping a number of foreign countries develop their economy, by building factories and manning regional offices in those countries. But recent development of terrorism especially against Korean businessmen is alarming. This report discusses the need for Korean enterprises heading overseas to prepare themselves with awareness of terrorism and possible protective measures against it, besides their routine pursuance of profits; and for the government and prospective enterprises to refrain from investing in those countries having active separatist movements. If an investment has become inevitable, a careful survey of the region in conflict should be conducted and self-protective measures should be put in place through security information exchange, emergency coordination and training of personnel, etc. This study will first review the past terrorist incidents involving employees of overseas Korean enterprises, and then will focuss on seeking effective measures on the basis of the reported incidents. In carrying out the study, related literature from both home and abroad have been used along with the preliminary materials reported and known on the Internet from recent incidents. 1. The separatist movements of minority groups Lately, minority separatist groups are increasingly resorting to terrorism to draw international attention with the political aim of gaining extended self rule or independence. 2. The state of terrorism against overseas Korean enterprises and Koreans Korean enterprises are now operating businesses, and having their own personnel stationed, in 85 countries including those in South East Asia and Middle East regions. In Sri Lanka, where a Korean enterprise recently became a target of terrorist bombing, there are 75 business firms from Korea and some 700 Korean employees are stationed as of August 1996. A total of 19 different terrorist incidents have taken place against Koreans abroad since 1990. 3. Terrorism preventive measures Terrorism preventive measures are discussed in two ways: measures by the government and by the enterprises. ${\blacktriangleleft}$ Measures by the government - Possible measures at governmental level can include collection and dissemination of terrorist activity information. Emphasis should be given to the information on North Korean activities in particular. ${\blacktriangleleft}$ Measures by individual enterprises - Organizational security plan must be established by individual enterprises and there should also be an increase of security budget. A reason for reluctant effort toward positive security plan is the perception that the security budget is not immediately linked to an increment of profit gain. Ensuring safety for overseas personnel is a fundamental obligation of an enterprise. Consultation and information exchange on security plan, and an emergency support system at a threat to security must be sought after and implemented. 4. Conclusion Today's terrorism varies widely depending on reasons and causes, and its means has become increasingly informationalized and scientific as well while its method is becoming more clandestine and violent. Terrorist organizations are increasingly aiming at enterprises for acquisition of budgets needed for their activities. Korean enterprises have extended their business realm to foreign countries since 1970, exposing themselves to terrorism. Enterprises and their employees, therefore, should establish their own security measures on the one hand while the government must provide general measures, on the other, for the protection of the life and property of Korean residents abroad from terrorist attacks. In this regard, set-up of a counter terrorist organization that coordinates the efforts of government authorities in various levels in planning and executing counter terrorist measures is desired. Since 1965, when the hostile North Korea began to step up its terrorist activities against South Koreans, there have been 7 different occasions of assassination attempt on South Korean presidents and some 500 cases of various kidnappings and attempted kidnappings. North Korea, nervous over the continued economic growth and social stabilization of South Korea, is now concentrating its efforts in the destruction and deterioration of the national power of South Korea for its earlier realization of reunification by force. The possibility of North Korean terrorism can be divided into external terrorist acts and internal terrorist acts depending on the nationality of the terrorists it uses. The external terrorist acts include those committed directly by North Korean agents in South Korea and abroad and those committed by dissident Koreans, hired Korean residents, or international professionals or independent international terrorists bought or instigated by North Korea. To protect the life and property of Korean enterprises and their employees abroad from the threat of terrorism, the government's administrative support and the organizational efforts of enterprises should necessarily be directed toward the planning of proper security measures and training of employees. Also, proper actions should be taken against possible terrorist acts toward Korean business employees abroad as long as there are ongoing hostilities from minority groups against their governments.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.