• Journal of Elementary Mathematics Education in Korea
• /
• v.16 no.3
• /
• pp.471-489
• /
• 2012

2007 Curriculum Revision adopted new Inquiry Activities in mathematical textbooks. So it is critical to analyze the problems of actual application of Inquiry activities in the classrooms. For this purpose, we analyzed the Inquiry activities of Measurement Area of the textbooks and find the appropriate solutions. Secondly, we develop the reorganization materials to fix and solve the existing problems found in the previous problem analysis, and apply the development materials and examine the effects afterwards. The results of the survey indicated that most of teachers are well aware of the importance of Inquiry Activities. However, many teachers answered that Inquiry activities does contain neither diverse strategic approaches nor solutions accommodating with various learning levels of students. The most difficult points to educate Inquiry Activities are that it is difficult to teach students based on individual learning level and that activities consist of mainly short answers that makes it difficult to do in-depth Inquiry Activities. Analyzing Inquiry Activities in the textbook shows that Inquiry Activities in some chapters were constructed as simple sentence questions or presented with the solving process in the questions themselves. The following application classes were implemented by partially taking advantage of the newly developed reorganization materials. Then, the effects were measured by before and after questionnaires, the survey to teachers, and the results of activities. The reorganization materials were effective at arousing the curiosity from students as well as enabling the natural ability-level driven classes.

• The KIPS Transactions:PartB
• /
• v.17B no.1
• /
• pp.93-98
• /
• 2010

In this paper, we introduce a system that summarizes product evaluation through linguistic analysis to effectively utilize explosively increasing product reviews. Our system analyzes polarities of product reviews by product features, based on which customers evaluate each product like 'design' and 'material' for a skirt product category. The system shows to customers a graph as a review summary that represents percentages of positive and negative reviews. We build an opinion word dictionary for each product feature through context based automatic expansion with small seed words, and judge polarity of reviews by product features with the extracted dictionary. In experiment using product reviews from online shopping malls, our system shows average accuracy of 69.8% in extracting judgemental word dictionary and 81.8% in polarity resolution for each sentence.

• The Journal of the Korea Contents Association
• /
• v.17 no.6
• /
• pp.408-414
• /
• 2017

This paper aims to investigate the value and resilience of Nogeoldae which was written at the end of Koryo dynasty and has been used as the most important foreign language education materials throughout the 500 years of Chosun dynasty. To this end, 106 volumes of dialogues, 12 of meeting, 17 of lodging, 21 of Daedo bound, 34 of Daedo lives and 11 of return in Nogeoldae are analyzed by an average length of the sentences, an average length of words, type-token ratio, number of words before main verbs and number of words before nouns to identify the progressive degree of the complexity. The result of the analysis shows that Nogeoldae presents a desired progressive complexity found in modern foreign language textbooks.

• The Journal of the Korea Contents Association
• /
• v.14 no.8
• /
• pp.528-541
• /
• 2014

The purpose of this study is to analyze Yun Chi-ho's English Diary both quantitatively and qualitatively. A corpus of 574 diary texts is created from his first and last years in an English speaking environment, and analyzed by two language analysis programs, RANGE and Coh-Metrix. His later diaries have more words in total, and longer average sentence length than his earlier diaries. Also, the Coh-Metrix indices in syntactic complexity and referential cohesion are higher in his later diaries. A qualitative analysis of 57 diary texts shows some improvement in his use of language forms. The most frequent topics of his journals are Christianity, everyday life, politics of Korea and his English studies. His constant effort to keep his journal and correspondence with foreigners, almost all in English, is estimated as one of the key factors for his successful English acquisition. Educational implications for EFL writing courses are discussed.

• Korean Journal of Cognitive Science
• /
• v.18 no.4
• /
• pp.457-483
• /
• 2007

Database Semantics (DBS) attempts to provide a comprehensive and integrated approach to human communication which seeks theory-implementation transparency. Two key components of DBS are Word bank as a data structure and left-Associative Grammar (LAG) as an algorithm. This study aims to provide a pilot implementation of Korean in DBS. First, it is shown how the three separate modules of grammar in DBS, namely, Hear, Think, and Speak, combine to form an integrated system that simulates a cognitive agent by making use of a simple Korean sentence as an example. Second, we provide a detailed analysis of the structure in Korean that is a characteristic of Korean involving numerals, classifiers, and nouns, thereby illustrating how DBS can be applied to Korean. We also discuss an issue raised in the literature concerning a problem that arises when we try to apply the LAG algorithm to the analysis of head-final language like Korean, and then discuss some possible solution to the problem.

• KIISE Transactions on Computing Practices
• /
• v.20 no.9
• /
• pp.492-497
• /
• 2014

In this paper, we propose a method of retrieving opinioned tweets in Twitter, which is the one of the popular Social Network Services and shares diverse opinions among various users. In typical opinion retrieval systems, they may consider the presence of sentiment phrases (subjectivity) as the important factor even if the subjective phrases are not related to a given query or speaker. To alleviate these problems, we utilized the syntactic structure of a sentence to identify the relationships between 1) subjectivity-query and 2) subjectivity-speaker and 3) the syntactic role of subjectivity. Besides, our learning-to-rank approach is trained to retrieve opinioned tweets based on query-relevance, textual features, user information, and Twitter-specific features. Experimental results on real world data show that our proposed method can achieve better performance than several baseline methods in terms of precision and nDCG.

• The KIPS Transactions:PartB
• /
• v.19B no.1
• /
• pp.43-52
• /
• 2012

Semantic role labeling is the research area analyzing the semantic relationship between elements in a sentence and it is considered as one of the most important semantic analysis research areas in natural language processing, such as word sense disambiguation. However, due to the lack of the relative linguistic resources, Korean semantic role labeling research has not been sufficiently developed. We, in this paper, propose an automatic predicate-argument analyzer to begin constructing the Korean PropBank which has been widely utilized in the semantic role labeling. The analyzer has mainly two components: the semantic lexical dictionary and the automatic predicate-argument extractor. The dictionary has the case frame information of verbs and the extractor is a module to decide the semantic class of the argument for a specific predicate existing in the syntactically annotated corpus. The analyzer developed in this research will help the construction of Korean PropBank and will finally play a big role in Korean semantic role labeling.

• The Journal of Information Systems
• /
• v.29 no.3
• /
• pp.237-251
• /
• 2020

Purpose The research was studied the hierarchical Hangul emotion index by organizing all the emotions which SNS users are thinking. As a preliminary study by the researcher, the English-based Plutchick (1980)'s emotional standard was reinterpreted in Korean, and a hashtag with implicit meaning on SNS was studied. To build a multidimensional emotion dictionary and classify three-dimensional emotions, an emotion seed was selected for the composition of seven emotion sets, and an emotion word dictionary was constructed by collecting SNS hashtags derived from each emotion seed. We also want to explore the priority of each Hangul emotion index. Design/methodology/approach In the process of transforming the matrix through the vector process of words constituting the sentence, weights were extracted using TF-IDF (Term Frequency Inverse Document Frequency), and the dimension reduction technique of the matrix in the emotion set was NMF (Nonnegative Matrix Factorization) algorithm. The emotional dimension was solved by using the characteristic value of the emotional word. The cosine distance algorithm was used to measure the distance between vectors by measuring the similarity of emotion words in the emotion set. Findings Customer needs analysis is a force to read changes in emotions, and Korean emotion word research is the customer's needs. In addition, the ranking of the emotion words within the emotion set will be a special criterion for reading the depth of the emotion. The sentiment index study of this research believes that by providing companies with effective information for emotional marketing, new business opportunities will be expanded and valued. In addition, if the emotion dictionary is eventually connected to the emotional DNA of the product, it will be possible to define the "emotional DNA", which is a set of emotions that the product should have.

• Journal of KIISE:Software and Applications
• /
• v.27 no.7
• /
• pp.784-793
• /
• 2000

The conventional morphological analysis of Korean language presents various morphological ambiguities because of its agglutinative nature. These ambiguities cause syntactic ambiguities and they make it difficult to select the correct parse tree. This problem is mainly related to the auxiliary predicate or bound noun in Korean. They have a strong relationship with the surrounding morphemes which are mostly functional morphemes that cannot stand alone. The combined morphemes have a syntactic or semantic role in the sentence. We extracted these morphemes from 0.2 million tagged words and classified these morphemes into three types. We call these morphemes a syntactic morpheme and regard them as an input unit of the syntactic analysis. This paper presents the syntactic morpheme is an efficient method for solving the following problems: 1) reduction of morphological ambiguities, 2) elimination of unnecessary partial parse trees during the parsing, and 3) reduction of syntactic ambiguity. Finally, the experimental results show that the syntactic morpheme is an essential unit for reducing morphological and syntactic ambiguity.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.197-208
• /
• 2020

Lexical ambiguity means that a word can be interpreted as two or more meanings, such as homonym and polysemy, and there are many cases of word sense ambiguation in words expressing emotions. In terms of projecting human psychology, these words convey specific and rich contexts, resulting in lexical ambiguity. In this study, we propose an emotional classification model that disambiguate word sense using bidirectional LSTM. It is based on the assumption that if the information of the surrounding context is fully reflected, the problem of lexical ambiguity can be solved and the emotions that the sentence wants to express can be expressed as one. Bidirectional LSTM is an algorithm that is frequently used in the field of natural language processing research requiring contextual information and is also intended to be used in this study to learn context. GloVe embedding is used as the embedding layer of this research model, and the performance of this model was verified compared to the model applied with LSTM and RNN algorithms. Such a framework could contribute to various fields, including marketing, which could connect the emotions of SNS users to their desire for consumption.