• Journal of Fashion Business
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• v.14 no.6
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• pp.11-22
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• 2010

The main purpose of this research was to examine the linguistic characteristics of domestic men's formal wear brand name. Four linguistic characteristics of language type, combined structure type of language, word class, length of brand name were investigated in this research and also examined the difference between brand type. For sample selection, the 209 men's fashion brands were selected from '2009 Korea Fashion Yearbook' and then, 25 brands which could not collect proper informations about the brand name or naming were excluded. Among total 184 men's brand names, 66 men's formal wear brands were selected and studied. For data analysis, quantitative evaluation of the frequency and qualitative evaluation have been used. The result as follows.; (1) Seven language types were found in domestic men's formal wear brand names. English has been used the most, then followed by Italian and French. (2) For combined structure type of brand name language, the single word used the most, followed by separately combined word type, artificially combined word, and unified word type. (3) The most frequently used the type of word class was noun, and followed by phrase, adjective, and verb. In the noun type, 6 different types which expressed a person, concrete & abstract entity, place, acronym, and neologic were found. For phrase, only noun type was appeared, however, 6 out of 20 phrases were abbreviated type. All eight adjective brand names implied an attributive character of the brand such as 'Dainty' or 'Solus(Solo)'. (4) The long name used most and then followed by normal and short length of brand name. Looking by the number of syllable, 4 syllables appeared the most and then followed by 3, 5, 6, 2 & 7 showed the same rate, and 8 syllables. (5) The result which compared the difference according to each brand type showed a difference in its language type, language combined style, word class, but length of brand name.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.489-497
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• 2007

Named entity translation plays an important role in many applications, such as information retrieval and machine translation. In this paper, we focus on translating person names, the most common type of name entity in Korean-Chinese cross language information retrieval (KCIR). Unlike other languages, Chinese uses characters (ideographs), which makes person name translation difficult because one syllable may map to several Chinese characters. We propose an effective hybrid person name translation method to improve the performance of KCIR. First, we use Wikipedia as a translation tool based on the inter-language links between the Korean edition and the Chinese or English editions. Second, we adopt the Naver people search engine to find the query name's Chinese or English translation. Third, we extract Korean-English transliteration pairs from Google snippets, and then search for the English-Chinese transliteration in the database of Taiwan's Central News Agency or in Google. The performance of KCIR using our method is over five times better than that of a dictionary-based system. The mean average precision is 0.3490 and the average recall is 0.7534. The method can deal with Chinese, Japanese, Korean, as well as non-CJK person name translation from Korean to Chinese. Hence, it substantially improves the performance of KCIR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.560-574
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• 2011

In this paper, we propose a hybrid method of Machine Learning (ML) algorithm and a rule-based algorithm to implement a lightweight Named Entity (NE) extraction system for Korean SMS text. NE extraction from Korean SMS text is a challenging theme due to the resource limitation on a mobile phone, corruptions in input text, need for extension to include personal information stored in a mobile phone, and sparsity of training data. The proposed hybrid method retaining the advantages of statistical ML and rule-based algorithms provides fully-automated procedures for the combination of ML approaches and their correction rules using a threshold-based soft decision function. The proposed method is applied to Korean SMS texts to extract person's names as well as location names which are key information in personal appointment management system. Our proposed system achieved 80.53% in F-measure in this domain, superior to those of the conventional ML approaches.

• The Journal of the Korea Contents Association
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• v.8 no.6
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• pp.261-268
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• 2008

An author of a paper is represented as his/her personal name in a bibliographic record. However, the use of names to indicate authors may deteriorate recall and precision of paper and/or author search, since the same name can be shared by many different individuals and a person can write his/her name in different forms. To solve this problem, it is required to disambiguate same-name author names into different persons. As features for author resolution, previous studies have exploited bibliographic attributes such as co-authors, titles, publication information, etc. This study attempts to apply email addresses of authors to disambiguate author names. For this, we first handle the extraction of email addresses from full-text papers, and then evaluate and analyze the effect of email addresses on author resolution using a large-scale test set.

• Journal of the Korean Society for information Management
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• v.25 no.3
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• pp.27-39
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• 2008

In bibliographic data, the use of personal names to indicate authors makes it difficult to specify a particular author since there are numerous authors whose personal names are the same. Resolving same-name author instances into different individuals is called author resolution, which consists of two steps: calculating author similarities and then clustering same-name author instances into different person groups. Author similarities are computed from similarities of author-related bibliographic features such as coauthors, titles of papers, publication information, using supervised or unsupervised methods. Supervised approaches employ machine learning techniques to automatically learn the author similarity function from author-resolved training samples. So far however, a few machine learning methods have been investigated for author resolution. This paper provides a comparative evaluation of a variety of recent high-performing machine learning techniques on author disambiguation, and compares several methods of processing author disambiguation features such as coauthors and titles of papers.

• Journal of Korean Society of Archives and Records Management
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• v.14 no.1
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• pp.7-24
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• 2014

The purpose of this study is to develop a thesaurus for women's oral history in modern Korea. Literature review and case studies for four thesauri were performed for this study with which a thesaurus was built based upon the index terms in oral history records. The process of developing the thesaurus consisted of five steps. First, there are 1,784 index terms from the oral history records by 53 modern Korean women were extracted and analyzed. Second, possible terms for the thesaurus were selected through regular meetings with experts in the fields of information organization and women's oral history. Third, relationships between terms were defined by focusing on equivalence, hierarchy, and association. Fourth, after developing a Web-based thesaurus management system, terms and relationships were input to the system. Fifth, terms and relationships were again reviewed by experts from the relevant fields. As a result, the thesaurus comprise of 1,076 terms and those terms were classified to 39 broad subject areas, including proper nouns, such as geographic names, places, person's names, corporate names, and others, and it will be expanded with more oral history records from other people during the same period.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• Cross-Cultural Studies
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• v.22
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• pp.177-201
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• 2011

The Lord of Mayan society was a person who was thought to be able to understand phenomena in the universe. And also the Lord could realize these activities of heaven on Earth through ritual. But the manifested ritual function of lordship was very different depends on Classic and Postclassic period. The Classic Mayan rituals were closely related to personal dignity, specially royal blood tradition meanwhile the Postclassic Mayans focused on public function of ritual. The ritual sacrifices of blood letting from their own body, manifested in Yaxchilan Lintel 24, 25 and 17, were focused on royal family's activity, showing the dignity of royal blood. The same ritual about the birth of family successor was observed at the Structure 5C4 from Postclassic ruin of Chichen Itza. However, this scene in focus, was two representative men and the answer of ancestor, not a special person. Also at the Lintel 1 of Temple of Four Lintels it was observed names of four Lords of Chichen Itza, their relationship, their action of firing to dedicate temple instead of writing long history of great royal family. All above shows that during Postclassic period the lords preferred a public function of their lordship than to dignify some royal persons through ritual.

• Korean Journal of Child Studies
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• v.27 no.1
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• pp.125-137
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• 2006

Of the 2,593 children picture books published in Korea between 2001 and 2003, 46% were Korean in origin, while 53.9% were Korean translations of books originally in English, Japanese, French, or German. This study compared 45 of the translations with the originals. Of these, 49.2% had contents not included in the originals, and 31.1 % had omitted contents. More over, 7.6% of the Korean versions were different in punctuation codes and signs, and 6.7% were different from originals in length of sentences. Most of the books were on general or global issues rather than culture-bound. However, among the English books, there were more than 20 cases different from Korean culture in person's names, external appearance, food, and/or clothing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1639-1658
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• 2019

Named entity recognition (NER) is an important technique for improving the performance of data mining and big data analytics. In previous studies, NER systems have been employed to identify named-entities using statistical methods based on prior information or linguistic features; however, such methods are limited in that they are unable to recognize unregistered or unlearned objects. In this paper, a method is proposed to extract objects, such as technologies, theories, or person names, by analyzing the collocation relationship between certain words that simultaneously appear around specific words in the abstracts of academic journals. The method is executed as follows. First, the data is preprocessed using data cleaning and sentence detection to separate the text into single sentences. Then, part-of-speech (POS) tagging is applied to the individual sentences. After this, the appearance and collocation information of the other POS tags is analyzed, excluding the entity candidates, such as nouns. Finally, an entity recognition model is created based on analyzing and classifying the information in the sentences.