• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.171-187
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• 2019

The study aims to analyze the posts of depression-related Facebook groups to understand major topics discussed by group users. Specifically, the purpose of the study is to identify the topics and keywords of the posts to understand what users discuss about depression. Depression is a mental disorder that is somewhat sensitive in the online community, which is characterized by accessibility, openness and anonymity. The researchers have implemented a natural language-based data analysis framework that includes components ranging from Facebook data collection to the automated extraction of topics. Using the framework, we collected and analyzed 885 posts created in the past one year from the largest Facebook depression group. To derive more complete and accurate topics, we combined both automated and manual (e.g., stop words removal, topic size determination) methods. Results indicate that users discuss a variety of topics including depression in general, human relations, mood and feeling, depression symptoms, suicide, medical references, family and etc.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.465-472
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• 2021

In this paper, we present an approach for detection of adverse drug reactions from drug reviews to compensate limitations of the spontaneous adverse drug reactions reporting system. Considering negative reviews usually contain adverse drug reactions, sentiment analysis on drug reviews was performed and extracted negative reviews. After then, MedDRA dictionary and named entity recognition were applied to the negative reviews to detect adverse drug reactions. For the experiment, drug reviews of Celecoxib, Naproxen, and Ibuprofen from 5 drug review sites, and analyzed. Our results showed that detection of adverse drug reactions is able to compensate to limitation of under-reporting in the spontaneous adverse drugs reactions reporting system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.41-46
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• 2023

Recently, big data analysis can use various techniques according to the development of machine learning. Big data collected in reality lacks an automated refining technique for the same or similar terms based on semantic analysis of the relationship between words. Since most of the big data is described in general sentences, it is difficult to understand the meaning and terms of the sentences. To solve these problems, it is necessary to understand the morphological analysis and meaning of sentences. Accordingly, NLP, a technique for analyzing natural language, can understand the word's relationship and sentences. Among the NLP techniques, the transformer has been proposed as a way to solve the disadvantages of RNN by using self-attention composed of an encoder-decoder structure of seq2seq. In this paper, transformers are used as a way to form associations between words in order to understand the words and phrases of sentences extracted from big data.

• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.115-124
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• 2023

This study explores the intersection of literary studies and artificial intelligence (AI), focusing on the common theme of human emotions to foster complementary advancements in both fields. By adopting a comparative perspective, the paper investigates emotion as a shared focal point, analyzing various emotion-related concepts from both literary and AI perspectives. Despite the scarcity of research on the fusion of AI and literary studies, this study pioneers an interdisciplinary approach within the humanities, anticipating future developments in AI. It proposes that literary sensibility can contribute to AI by formalizing subjective literary emotions, thereby enhancing AI's understanding of complex human emotions. This paper's methodology involves the terminology-centered extraction of emotions, aiming to blend subjective imagination with objective technology. This fusion is expected to not only deepen AI's comprehension of human complexities but also broaden literary research by rapidly analyzing diverse human data. The study emphasizes the need for a collaborative dialogue between literature and engineering, recognizing each field's limitations while pursuing a convergent enhancement that transcends these boundaries.

• Journal of Korean Library and Information Science Society
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• v.35 no.4
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• pp.451-467
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• 2004

This paper is intended to present the theoretical analyses on automatic indexing, which is vital in the process of constructing a thesaurus browser, and clustering algorithms to construct hierarchical relations among terms as well as the methods for the automatic construction of a thesaurus browser. The methods to select the index term automatically in the web documents are studied by surveying the methods for analyzing and processing metadata which conforms to bibliographical roles of traditional paper documents in web documents. Also, the result of the study suggests to adding or involving the metadata in web documents, using the metadata automatic editor because metadata is not listed in most of the web documents.

• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.25-43
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• 2009

The purpose of this study is to suggest how the classic thesaurus structure of terms and links can be mined and updated from Wikipedia encyclopedia, which is the best practice of collective intelligence. In a comparison with ASIS&T thesaurus, it was found that Wikipedia contains a substantial coverage of domain-specific concepts and semantic relations. Furthermore, it was resulted that the structural characteristics of Wikipedia, such as redirects, categories, and mutual links are suitable to extract semantic relationships of thesaurus. It is needed to apply to update various thesauri, including multilingual thesaurus, in order to generalize the results of this research.

• Journal of Korean Medical classics
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• v.33 no.4
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• pp.67-81
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• 2020

Objectives : This research aims to suggest a automatic data extract method for herbal formula combinations from medical classics' texts. Methods : This research was carried out by using Access of Microsoft Office 365 in Windows 10 of Microsoft. The subject text for extraction was 『Euijongsonik』. Using data sets of herb and dosage terminology, herbal medicinals and their dosages were extracted. Afterwards, using the position value of the character string, the formula combinations were automatically extracted. Results :The PC environment of this research was Intel Core i7-1065G7 CPU 1.30GHz, with 8GB of RAM and a Windows 10 64bit operation system. Out of 6,115 verses, 19,277 herb-dosage combinations were extracted. Conclusions : In this research, it was demonstrated that in the case of classical texts that are available as data, knowledge on herbal medicine could be extracted without human or material resources. This suggests an applicability of classical text knowledge to clinical practice.

• Annual Conference on Human and Language Technology
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• 1998.10c
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• pp.73-80
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• 1998

본 연구는 자연어 질의의 형태 및 구문 정보를 바탕으로 불리언 질의를 생성하는데 그 목적을 둔다. 일반적으로 대부분의 상용정보검색시스템은 입력형식을 검색성능이 종은 불리언 형태로 하고 있으나, 일반 사용자는 자신이 원하는 정보를 불리언 형태로 표현하는데 익숙하지 않다. 그러므로 본 정보검색시스템은 자연어 질의를 기본 입력형태로 하여 사용자의 편의성을 높이고, 이 질의를 범주문법에 기반한 구문분석 결과에 의해 복합명사를 고려한 불리언 형태로 변환하여 검색을 수행함으로써 시스템의 검색 성능의 향상을 도모하였다. 정보검색 실험용 데이터 모음인 KTSET2.0으로 실험한 결과 본 논문에서 제안한 자연어 질의로부터 자동 생성된 불리언 질의의 검객성능이 KTSET2.0에서 제공하는 수동으로 추출한 불리언 질의보다 8% 더 우수한 성능을 보였고, 기존 자연어질의 시스템이 수용해온 방법인 형태소 분석을 거쳐 불용어를 제거한 후 Vector 모델을 적용하여 검색을 수행한 경우보다는 23% 더 나은 성능을 보였다.

• Journal of The Korean Association of Information Education
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• v.14 no.4
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• pp.537-545
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• 2010

The purpose of this paper is to develop mathematics learning contents for elementary school 3rd graders and to verify the educational effectiveness of contents developed. An ADDIE model was applied to develop mathematics learning contents based on storytelling for concept learning. After extracting 54 concepts from the mathematics curriculum, researchers designed strategies using concepts that were combined with context which is familiar to young students. Researchers implemented a survey and interview to students and teachers to verify the effectiveness of contents. As a result, the understanding, interest, concentration, and expectation of students toward the contents developed were very high, and teachers also mentioned that these contents could be very useful teaching materials for motivation.

• 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.