• Journal of Korean Library and Information Science Society
• /
• v.44 no.4
• /
• pp.233-254
• /
• 2013

Collecting terms in thesaurus construction work is the most difficult. A dictionary is thesaurus can be used as an excellent term acquisition. Reflect faithfully the provisions of Korean literary "Standard Korean Dictionary" is a standard dictionary of the Korea. The "Standard Korean Dictionary" is simply the definition of entry, as well as a wide range of information about the term because it contains a systematic, it can be used to build a thesaurus. In this study, the "Standard Korean Dictionary" has the relevant information using a variety of terms, it is defined as the thesaurus term relationship. In addition, the separation of the term, equal relationships and hierarchical set of relationships, the use of qualifiers, North Korean issues, the issue presented in thesaurus construction, and suggest ways to solve the problem.

• Journal for History of Mathematics
• /
• v.33 no.4
• /
• pp.237-257
• /
• 2020

In this paper, we classify the mathematical terms in Korean Standard Unabridged Dictionary into four groups; ① group 1 consists of the terms which coincide with the mathematical terms in the 2015 Editing Material, ② group 2 consists of the terms which are synonyms or old terms or inflection forms of the mathematical terms in the Editing Material, ③ group 3 consists of the terms which do not belong to group 1 or group 2, but relate to the elementary or secondary school mathematics, ④ group 4 consists of the terms which do not relate to the elementary or secondary school mathematics. And then we make a comparative study with the mathematical terms in the Editing Material. In this study, we find out the mathematical terms in the Editing Material, but not in Korean Standard Unabridged Dictionary. And by using synonyms and old terms of the mathematical terms in the Editing Material we guess the rough tendency which terms belong to the Editing Material. By investigating the terms in group 3 and 4, we find out the mathematical terms which may belong to the Editing Material. We also find out the wrong or inconsistent explanations in Korean Standard Unabridged Dictionary.

• Phonetics and Speech Sciences
• /
• v.1 no.2
• /
• pp.151-162
• /
• 2009

This paper aims to investigate how tensification preference has changed over time and discuss how appropriately tensification preference is reflected in Principles of Standard Pronunciation and Standard Korean Language Dictionary. For this research, a questionnaire survey of tensification preference was conducted. 173 test words were used and 156 native Seoul speakers participated in this survey. The results have shown that tensification preference has gradually increased from older to younger generations. In addition, Principles of Standard Pronunciation and Standard Korean Language Dictionary do not reflect real pronunciation appropriately. Therefore, some ways of incorporating the actual pronunciation of Seoul speakers in the Principles of Standard Pronunciation and the Standard Korean Language Dictionary are suggested.

• Cross-Cultural Studies
• /
• v.38
• /
• pp.405-431
• /
• 2015

The purpose of this paper is to investigate how to decide pronunciation of loanwords in Korean language. There has not been a regulation for pronunciation of loanwords in Korean language. Even the dictionary published by the government does not provide any information about the pronunciation of loanwords. In this paper, some actual solutions are suggested for the pronunciation of loanwords. Korean language has Regulations of Standard Korean, Korean Orthography, Regulations on Hangeul Transcriptions on Loanwords and Pronunciation Methods of Standard Korean. These language standards could help to decide pronunciation of loanwords. Some pronunciations which could not be regulated by them must be presented in the standard pronunciation dictionary. For example, glottalization rule of 's' in many loanwords could be presented in the description of each loanword in the dictionary. However the pronunciation of loanwords must be similar to the spelling. If various pronunciations are allowed to one spelling, then people will be so confused by the discrepancy between pronunciation and spelling of loanwords.

• Korean Journal of Cognitive Science
• /
• v.7 no.4
• /
• pp.75-93
• /
• 1996

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.2
• /
• pp.214-221
• /
• 2015

A new classification system with 9 main categories and 56 subcategories for the Open Korean Knowledge Dictionary is proposed. The classification system setup is to prepare for the standard classification system to be used to manage effectively vast of terminologies which were published in the Open Korean Knowledge Dictionary and is meant to enhance the fifteen-year old classification system for the standard korean great dictionary to match up to the trend of the modern terminology. The new terminology classification system covering all the academic areas such as humanity, sociology, politics, science, medicine, agriculture, engineering, etc, is designed and proposed after investigating several classification systems. The classification system setup procedures follow as ${\circ}$ The classification system is designed and planed by both the classification system and the academic expert. ${\circ}$ Classification system design covers all the academic areas following National Science and Technology standard classification system after investigating several classification systems such as the National Research Foundation, National Science and Technology Standard Act, Ministry of Knowledge Economy. ${\circ}$ Poll and survey is made to collect comments from total 93 members of several academic areas. ${\circ}$ The poll result is reviewed among working group members and utilized to update the new terminology classification system. Reclassifications are made for the around 200,000 terms in electricity, computer, medicine, pharmacy, biology, and economics according to the new terminology classification system.

• MALSORI
• /
• no.51
• /
• pp.17-37
• /
• 2004

The purpose of this dissertation is to make the general korean speakers to learn the standard pronunciations. But it is in existence that the obstructions of the command of the standard pronunciations. They are the mistake in the education course on the korean pronunciations, the teacher's capability and the mass communications's duplicity. To overcome this obstructions, we must concentrate our efforts on the propagation of the standard pronunciations. To propagate of the standard pronunciations we can take a several method. These are the presentation of the pronunciation mistakes, audio-visual teaching, the presentation of the pronunciation principles and the use of the korean dictionary. The standard pronunciations are different from the pronunciations of the general korean speakers in many respects. So we can't make an accurate estimate of the pronunciation's changes. No one knows what will happen in the future about the korean pronunciations. But we must teach the standard pronunciations to the general korean speakers. The standard pronunciations are offically valid in the present time.

• The Journal of the Korea Contents Association
• /
• v.16 no.4
• /
• pp.418-425
• /
• 2016

This paper presents the part-of-speech set which is highly efficient at knowledge-based word sense disambiguation for Korean nouns. 174,000 sentences extracted for test set from Sejong semantic tagged corpus whose sense is based on Standard korean dictionary. We disambiguate selected nouns in test set using glosses and examples in Standard Korean dictionary. 15 part-of-speeches which give the best performance for all test set and 17 part-of-speeches which give the best performance for accuracy average of selected nouns are selected. We obtain 12% more performance by those part-of-speech sets than by full 45 part-of-speech set.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.1-13
• /
• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Proceedings of the KSPS conference
• /
• 2003.05a
• /
• pp.168-171
• /
• 2003

This study is about modeling pronunciation dictionary necessary for PLU(phoneme like unit) level word recognition. The recognition of nonnative speakers' pronunciation enables an automatic diagnosis and an error detection which are the core of English pronunciation tutoring system. The above system needs two pronunciation dictionaries. One is for representing standard English pronunciation. The other is for representing Korean speakers' English Pronunciation. Both dictionaries are integrated to generate pronunciation networks for variants.