• Language and Information
• /
• v.2 no.2
• /
• pp.121-154
• /
• 1998

The morphological analyzer is expected to tell attested word forms from imaginable yet unattested ones. An account of the inflectional morphology of English verbs is given in the framework of Word-and-Paradigm morphology, developed mainly by Matthews (1972, 1974, 1991) and further by Aronoff (1994) and Zwicky (1985, 1988), which is free of overrecognition. Thirteen inflectional classes are identified according to the patterns each of them exhibits in filling the slots in the paradigm. Peculiarity in orthography is also considered in assigning each verb lexeme to a class. Modules of a C program which gives associated morphosyntactic properties to all and only attested verb forms are written so that details of this framework can be evaluated explicitly. This program is shown to be superior to existing programs in economy and in the generality it achieves.

• Korea Journal of Hospital Management
• /
• v.17 no.3
• /
• pp.57-82
• /
• 2012

The primary purpose of this study is to examine consumers'probing actions to see what information sources consumers search for medical information when there are diverse medical service information channels, and classify consumers by information source. Its secondary purpose is to understand trust of information and attitude toward information by consumer type, value of medical service, satisfaction with medical service, and word-of-mouth intention. This study will concretely identify information utilization patterns of medical consumers, and explain the unique characteristics and behavior of segmented types of medical consumers. The significance of this study lies in the search for ways to establish information channels trusted by consumers for building an efficient medical service market in the future. The results of this study show that consumers were classified by the latent class analysis(LCA) into 5 types: low-level information seekers, word-of-mouth information seekers, mass media information seekers, digital information seekers and diverse information seekers. The reliability of information sources by type of medical consumer was statistically significant, and in the analysis of differences in consumer attitude, there was a statistically significant difference in cognitive responses. The value of medical service was statistically significant in health recovery and medical service word-of-mouth intention.

• Journal of the Korean Society for information Management
• /
• v.40 no.4
• /
• pp.307-327
• /
• 2023

To analyze the impact of word embedding on book titles, this study utilized word embedding models (Word2vec, GloVe, fastText) to generate embedding vectors from book titles. These vectors were then used as classification features for automatic classification. The classifier utilized the k-nearest neighbors (kNN) algorithm, with the categories for automatic classification based on the DDC (Dewey Decimal Classification) main class 300 assigned by libraries to books. In the automatic classification experiment applying word embeddings to book titles, the Skip-gram architectures of Word2vec and fastText showed better results in the automatic classification performance of the kNN classifier compared to the TF-IDF features. In the optimization of various hyperparameters across the three models, the Skip-gram architecture of the fastText model demonstrated overall good performance. Specifically, better performance was observed when using hierarchical softmax and larger embedding dimensions as hyperparameters in this model. From a performance perspective, fastText can generate embeddings for substrings or subwords using the n-gram method, which has been shown to increase recall. The Skip-gram architecture of the Word2vec model generally showed good performance at low dimensions(size 300) and with small sizes of negative sampling (3 or 5).

• Educational Technology International
• /
• v.21 no.2
• /
• pp.193-216
• /
• 2020

This study investigated university students' perspectives on good class and instructional practices through social network analysis. The subjects were 321 students in the third and fourth academic years in a Korean university. The subjects completed four open-ended questions, asking about experience of good class, good instructors' teaching practice, and their feelings and attitudes when participating in good class. As social network analysis, KrKwic (Korea Key Words in Context) was used to compute word frequencies and analyze semantic network structures and Ucinet Netdraw to assess centrality in the social network, consisting of degree centrality, closeness centrality, and between centrality. The results are as follows. First, students showed 5 keywords to depict what good class is, including 'understanding', 'example', 'video', 'interest', and 'communication'. Second, the characteristics of teaching methods by professors who practice good class indicate 'assignments', 'questions', 'understanding', 'example', and 'feedback'. Third, the top 5 keywords of students' attitudes as participating in good class are 'active', 'participation', 'focus', 'listening', and 'asking'. Last, keywords depicting desirable class that students most wanted to take next time are 'assignments', 'rewards', 'understanding', 'difficulty', and 'interest'. The findings from this study include the meanings of the semantic network structures of words in the text making up messages. Also this study can provide empirical evidence for educators and educational practitioners in higher education to create effective learning environments.

• Journal of Information Technology Services
• /
• v.16 no.4
• /
• pp.149-160
• /
• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.4
• /
• pp.103-108
• /
• 2021

Knowledge graph is a technology that improves search results by using semantic information based on various resources. Therefore, due to these advantages, the knowledge graph is being defined as one of the core research technologies to provide AI-based services recently. However, in the case of the knowledge graph, since the form of knowledge collected from various service domains is defined as plain text, it is very important to be able to analyze the text and understand its meaning. Recently, various lexical dictionaries have been proposed together with the knowledge graph, but since most lexical dictionaries are defined in a language other than Korean, there is a problem in that the corresponding language dictionary cannot be used when providing a Korean knowledge service. To solve this problem, this paper proposes an ontology based on the parts of speech of Korean. The proposed ontology uses 9 parts of speech in Korean to enable the interpretation of words and their semantic meaning through a semantic connection between word class and word class. We also studied various scenarios to apply the proposed ontology to vehicle services.

• International Journal of Advanced Culture Technology
• /
• v.7 no.4
• /
• pp.49-55
• /
• 2019

In the age of information, newly produced knowledge is mostly written in English. Therefore, there has been a strong demand for English language learning in the EFL context. However, most EFL learners possess a lack of interest and motivation in the text-based reading class. In this educational context, film is one of the most widely used materials in English reading classes considering that modern learners are predominantly familiar with various audiovisual materials. The purpose of this study is to investigate how Korean EFL learners experienced in the film-based reading class. Specifically, this study aims to analyze the EFL students' perceptions about the class and learning strategies that they used during the class. In order to comprehensively interpret the EFL learners' experiences in the classroom, a coding system consisting of five categories was developed: report, emotion, reflection, evaluation, future plans. The results of data analysis showed that the use of movies in English reading classes had positive effects on reading comprehension and inference of word meaning. The most frequently used learning strategies were affective strategies which helped them control their emotion, attitude, motivations and values, whereas memorization strategies were rarely used. In this respect, this study suggests that the use of movies in the EFL reading classroom encourage students' attention and help them obtain and activate schema which is useful in gaining a better understanding of text-based reading materials.

• School Mathematics
• /
• v.19 no.3
• /
• pp.571-591
• /
• 2017

The purpose of this study is to understand the characteristics of mathematical discourse about the length in the class that learns the length of the curve defined by definite integral. For this purpose, this study examined the discourse about length by paying attention to the usage of the word 'length' in the class participants based on the communicative approach. As a result of the research, it was confirmed that the word 'length' is used in three usages - colloquial, operational, and structural usage - in the process of communicating with the discourse participants. Particularly, each participant did not recognize the difference even though they used different usage words, and this resulted in ineffective communication. This study emphasizes the fact that the difference in usage of words used by participants reduces the effectiveness of communication. However, if discourse participants pay attention to the differences of these usages and recognize that there are different discourses, this study suggests that meta - level learning can be possible by overcoming communication discontinuities and resolving conflicts.

• Journal of Applied and Pure Mathematics
• /
• v.4 no.1_2
• /
• pp.27-36
• /
• 2022

An innovation diffusion model is proposed model consists of three classes, namely, a non-adopter class, adopter class innovation-I, and adopter class innovation-II in a partially competitive and cooperative market. The proposed model is analyzed with the help of the qualitative theory of a system of ordinary differential equations. Basic influence numbers associated with first and second innovation $R_{0_1}$ and $R_{0_2}$ respectively in the absence of each other are quantified. Then the overall basic influence number (R₀) of the system is assessed for analyzing stability in the market in different situations. Sensitivity analysis of basic influence numbers associated with first and second innovation in the absence of each other is carried out. Numerical simulation supports our analytical findings.

• Bulletin of the Korean Mathematical Society
• /
• v.61 no.3
• /
• pp.585-595
• /
• 2024

We show that a pseudo-Anosov map constructed as a product of the large power of Dehn twists of two filling curves always has a geodesic axis on the curve graph of the surface. We also obtain estimates of the stable translation length of a pseudo-Anosov map, when two filling curves are replaced by multicurves. Three main applications of our theorem are the following: (a) determining which word realizes the minimal translation length on the curve graph within a specific class of words, (b) giving a new class of pseudo-Anosov maps optimizing the ratio of stable translation lengths on the curve graph to that on Teichmüller space, (c) giving a partial answer of how much power is needed for Dehn twists to generate right-angled Artin subgroup of the mapping class group.