• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.5
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• pp.1019-1035
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• 2020

This study examines the millennial generation, who express themselves and share information on social media after experiencing constantly changing 'hot places' (places of interest) in contemporary cities, with the goal of analyzing space consumption behaviors. Data were collected via an Instagram crawler application developed with Python 3.4 administered to 19,262 posts using the term 'hot places' from November 1 and December 15, 2019. Issues were derived from a text mining technique using Textom 2.0; in addition, semantic network analysis using Ucinet6 and the NetDraw program were also conducted. The results are as follows. First, a frequency analysis of keywords for hot places indicated words frequently found in nouns were related to food, local names, SNS and timing. Words related to positive emotions felt in experience, and words related to behavior in hot places appeared in predicate. Based on importance, communication is the most important keyword and influenced all issues. Second, the results of visualization of semantic network analysis revealed four categories in the scope of the definition of "hot place": (1) culinary exploration, (2) atmosphere of cafés, (3) happy daily life of 'me' expressed in images, (4) emotional photos.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.135-143
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• 2022

In order to convert Korean folk paintings called Minhwa, a part of traditional Korean heritage, into DBs, it is necessary to design a classification system suitable for the characteristics of folk paintings. A classification system and the generating of unique codes are required to classify and save them. To realize this, a basic classification system was created by listing objects depicted in folk paintings, and keywords were extracted by reclassifying them for each object. In order to assign a unique code to each piece, we organize the English names of each Minhwa since the English names of the folk painting contain the names of objects. The code name is extracted by applying the order of nouns and consonant priority rules in English names and attaching five Arabic numerals. These codes are later assigned to each image file stored in the database and are input together with the keyword. The Minhwa DB constructed in this way enables storage and search centered on objects and keywords and the intuitive inferring of the type of object from the code name.

• CELLMED
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• v.13 no.2
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• pp.3.1-3.21
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• 2023

In this study, the drinking culture of the Choseon dynasty is examined with the text frequency analysis technique on the entire 『Choseonwangjosilok (朝鮮王朝實錄)』. This study examined a total of 1,968 volumes and 948 books about 27 kings of Choseon , which spans a total of 518 years, through web crawling on the National Institute of Korean History website. Python 3.8 was used to extract sentences related to alcohol, Rhino 1.4.5 was used for morphological analysis to extract nouns, and Gephi 0.9.2 was used for semantic network analysis. According to 『Choseonwangjosilok (朝鮮王朝實錄)』 about alcohol culture, the results of the analysis are as follow: Alcoholic beverages were more often used in court or in ritual ceremonies rather than those based on specific ingredients or manufacturing methods commonly used by the general public. regarding the ruling class through semantic network analysis l in the 『Choseonwangjosilok (朝鮮王朝實錄)』, the Choseon dynasty was found to be highly associated with political issues related to maintaining the power relations within the Korean royal court system. At times, alcohol was used to maintain personal relationships, while at other times it was seen as an essential item in state ceremonies. It was also used as a highly political means to maintain and strengthen national power.

• MALSORI
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• v.68
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• pp.115-126
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• 2008

In this paper, we propose a vocabulary coverage improvement method for embedded continuous speech recognition (CSR) using knowledgebase. A vocabulary in CSR is normally derived from a word frequency list. Therefore, the vocabulary coverage is dependent on a corpus. In the previous research, we presented an improved way of vocabulary generation using part-of-speech (POS) tagged corpus. We analyzed all words paired with 101 among 152 POS tags and decided on a set of words which have to be included in vocabularies of any size. However, for the other 51 POS tags (e.g. nouns, verbs), the vocabulary inclusion of words paired with such POS tags are still based on word frequency counted on a corpus. In this paper, we propose a corpus independent word inclusion method for noun-, verb-, and named entity(NE)-related POS tags using knowledgebase. For noun-related POS tags, we generate synonym groups and analyze their relative importance using Google search. Then, we categorize verbs by lemma and analyze relative importance of each lemma from a pre-analyzed statistic for verbs. We determine the inclusion order of NEs through Google search. The proposed method shows better coverage for the test short message service (SMS) text corpus.

• The Journal of Information Systems
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• v.28 no.4
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• pp.155-174
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• 2019

Purpose Social interest in financial statement notes has recently increased. However, contrary to the keen interest in financial statement notes, there is no morphological analyzer for accounting terms, which is why researchers are having considerable difficulty in carrying out research. In this study, we build a morphological analyzer for accounting related text mining techniques. This morphological analyzer can handle accounting terms like financial statements and we expect it to serve as a springboard for growth in the text mining research field. Design/methodology/approach In this study, we build customized korean morphological analyzer to extract proper accounting terms. First, we collect Company's Financial Statement notes, financial information data published by KPFIS(Korea Public Finance Information Service), K-IFRS accounting terms data. Second, we cleaning and tokeninzing and removing stopwords. Third, we customize morphological analyzer using n-gram methodology. Findings Existing morphological analyzer cannot extract accounting terms because it split accounting terms to many nouns. In this study, the new customized morphological analyzer can detect more appropriate accounting terms comparing to the existing morphological analyzer. We found that accounting words that were not detected by existing morphological analyzers were detected in new customized morphological analyzers.

• The Transactions of the Korea Information Processing Society
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• v.2 no.2
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• pp.229-236
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• 1995

The conceptual world of sentence is composed f substantives(nouns) and verbal. The verbal is a semantic center of sentence, the substantives are placed under control of verbal, and they are combined in a various way. In this paper, the structural relation of verbal and substantives are analyzed and the phrase unit sentence which is derived from the result of morphological analysis is interpreted by a variable pattern net. This variable pattern net analyzes the phrases syntactically and semantically and extracts conceptual units of clausal form. This paper expands the traditionally restricted Horn clause theory to the general sentence, separates a simple sentence from a complex sentence automatically, constructs knowledge base by clausal form of logical conceptual units, and applies it to a question-answering system.

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.247-251
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• 2001

명사 의미 부류 체계는 언어 처리의 다양한 분야에서 그 필요성이 부각되고 있다. 예를 들어, 기계 번역에 있어서의 단어 의미의 중의성 해소(word sense disambiguation), 정보검색 시스템에서도 재현율과 정확률의 향상, 추론 시스템 등을 위하여 명사 의미 부류는 중요한 역할을 한다. 명사 의미 부류 체계의 이러한 중요성 때문에 여러 온톨로지(ontology)가 기존에 구축되어 있다. 그런데 이러한 온톨로지들은 대개 순수한 개념적 기준에 입각한 것이며 단어의 통사적 특성을 별로 고려하고 있지 않다. 정보검색 시스템이나 추론 시스템의 경우에는 통사적 고려가 별로 중요하지 않을 수 있으나 기계번역의 경우 통사적 특성에 대한 고려가 매우 중요하다. 이러한 점에 주목하여 21세기 세종계획 전자사전 분과에서는 개념적 기준과 통사적 기준을 모두 고려하여 명사 의미 부류 체계를 구축하고 있다. 즉, 해당 부류에 속하는 명사들이 결합할 수 있는 술어(적정 술어) 등의 통사적 요인을 중요시하여 명사들을 분류하고 있는 것이다. 이에 따라 세종 체언 사전의 모든 명사들에 대해 의미부류 정보가 주어지고, 용언 사전의 용언의 각 논항에 대한 선택제약 정보도 이 명사 의미부류 체계를 이용하여 제시되고 있다. 이러한 정보들은 한국어 처리에 중요한 자료로 이용될 것이다.

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.341-347
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• 2001

세종전자사전이 궁극적으로 범용전자사전을 지향한다는 점에 비추어 볼 때, 텍스트 자동 분석과 생성, 정보 검색 및 자동 번역 등에 활용될 데이터베이스로서의 전자사전은 자연 언어 어휘의 내적 구조와 기능방식에 관한 정보들, 가령 음운 통사 의미 화용적 가치와 실현 조건 등에 관한 정보들을 체계적이고도 정교하게 담고 있어야만 한다. 의존명사, 대명사, 수사 범주에 속하는 언어 단위들은 단일 명사와 구별되는 어휘 통사적 속성들을 지니며, 사전의 기술 구조에는 그 정보 값들을 체계적으로 명시화할 수 있는 정보 항목과 표상 구조가 설정되어야 한다. 가령 의존명사처럼 통사 의미적 자율성을 지니지 않는 언어 요소의 경우, 어휘 관계 정보보다는 인접하는 여타 언어 단위들과의 호응관계나 결합제약 조건들이 더 중요한 정보일 수 있다. 본 사전이 체언사전의 하위사전으로 별도로 구축되는 것은 단일어 사전에서 그러한 정보들을 효과적으로 표상하기 어렵기 때문이다. 그러나 본 사전은 실제적으로는 체언사전에 통합되어 운영된다는 점에서 이중적 지위를 누린다고 하겠다.

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.348-355
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• 2001

세종전자사전은 한국어 어휘의 정보를 총체적으로 표상하는 전산어휘자료체이다. 세종전자사전은 궁극적으로 다양한 유형과 기능의 한국어 자동처리에의 활용을 목표로 한다. 세종체언전자사전은 최종적으로 5만여 항목을 대상으로 구축될 것이다. 세종명사전자사전은 전산적 효율성을 고려하여 명사 어휘의 정보를 8개의 하위정보구획과 50여개의 세부정보항목으로 구분하여 표상한다. 특히, 명사의 어휘 통사적인 결합에 관한 정보는 한국어 명사구와 문장의 자동생성에 직접 활용될 수 있는 정보이다. 명사는 수식어 요소 또는 조사와 결합하여 명사구를 형성하고, 동사 및 형용사와 결합하여 문장의 생성에 참여한다. 개별 명사들은 이 과정에서 다양한 제약적인 양상들을 보여주고 있으며, 세종명사전자사전은 이 정보들을 명시적으로 표상한다. 또한 명사는 기능동사와 결합하는 술어명사와 그렇지 않은 비술어명사로 구분이 된다. 술어 명사가 기능동사와 결합하여 문장을 형성할 때, 명사와 그 논항들은 다양한 통사적 기능을 담당한다. 또한 술어명사는 논항과 결합하여 명사구를 형성한다. 그러나, 술어명사의 명사구 형성과 기능동사의 결합은 불규칙적이고, 명사와 기능동사의 의미적 특성에 따라 다양한 제약이 발견된다. 이 정보들의 정밀한 표상은 개별 술어명사로부터 생성될 수 있는 가능한 명사구와 문장의 형태를 구체적으로 밝혀주게 된다. 세종명사전자사전의 어휘 통사적인 결합에 관한 정보들은 명사구 또는 문장의 자동생성의 정확성과 효율성을 높여줄 것이다.

• Language and Information
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• v.13 no.1
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• pp.77-92
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• 2009

Geis (1973) has provided various properties of the subjects and by + Gerund Phrase (GerP) in English causative constructions. Among them, the two main issues of Geis's analysis are as follows: unlike Lakoff (1965; 1966), the subject of English causative constructions, including causative-inchoative verbs such as liquefy, first of all, should be acts or events, not persons, and the by + GerP in the construction is a complement of the causative verbs. In addition to these issues, Geis has provided various data exhibiting other idiosyncratic properties and proposed some transformational rules such as the Agent Creation Rule and rule orderings to explain them. Against Geis's claim, I propose that English causative verbs require either Proper nouns or GerP subjects and that the by + GerP in the constructions as a Verbal Modifier needs Gerunds, whose understood Affective-agent subject is identical to the subject of causative verbs with respect to the semantic index value. This enables us to solve the two main issues. At the same time, the other properties Geis mentioned also can be easily accounted for in Head-driven Phrase Structure Grammar (HPSG) by positing a few lexical constraints. On this basis, it is shown that given the few lexical constraints and existing grammatical tools in HPSG, the constraint-based analysis proposed here gives a simpler explanation of the properties of English causative constructions provided by Geis without transformational rules and rule orderings.