• Journal of the Korean housing association
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• v.17 no.5
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• pp.27-37
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• 2006

This paper analyzed statistically two corpora that were constructed from the texts about house designs written by Korean architects and PA Awards architects. The main results are as follows; (1) The numbers of words in Korean house-design corpus were 9,352 and those of words in PA Awards house design corpus were 2,379. The former were 18.7% and the latter 4.8% of about 50,000 words regarded as the rest using scale in actual life. (2) When the architects described their house designs, the lexicon-concentration phenomenon was pervasive in both groups. Therefore, we can infer that the high-frequency lexicons are very important in house design. (3) The architects' behaviour patterns of using the house-design lexicons, went by rules according to the word frequency order. The tendency formulas of them had the $R^{2}$ values which were more than 90%. (4) In Korean house design corpus, the high frequency lexicons were '공간', '층', '주택', '집', '대지', '거실', and '실'. In PA awards house design corpus, they were 'house','room','space','living','wall','level' and 'area'. From these results, We can tell that 'space' is the highest frequency word in house design of the two groups, and that '대지 ' and 'wall' are the words that reveal well the differences between the two groups.

• The Mathematical Education
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• v.61 no.4
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• pp.559-579
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• 2022

Research on lexicon and language provides insights into the interests, values and practices of a community where individuals use the language. The International Classroom Lexicon Project, in which ten countries participated, identified own country's mathematics teaching and learning lexicons by investigating mathematics classroom instruction from teachers' perspectives in a speaking-oriented community. This study, as an extension of the International Classroom Lexicon Project research, investigated pedagogical lexicons used in 「Mathematics and Education」 journals specialized for Korean professional mathematics teachers published by the Korean Society of Teachers of Mathematics. Using the text mining approach, we also traced how these pedegogical lexicons have changed quantitatively over the past 10 years with a diachronic perspective. As a results, several novel terms were found in the writing-oriented community, which were not identified in the speaking-oriented community. In addition, we could discover some pedagogical lexicons have increased statistically significantly and some lexicons appeared(increased) rapidly across years. This implies the teacher community's values and zeitgeist by reflecting these changes in the sociocultural, incidental and social changing (i.e., periodical change) contexts. This study has value as a first step in understanding zeitgeist for mathematics education in Korean mathematics teacher community according to changes of times over the past 10 years. Also, this study contributes to the methodological insights: the text mining technique provides a methodological contribution to researching changes in interests, values and zeitgeist according to these changes in the times.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.103-110
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• 2015

Sentiment lexicon is an essential element for expressing sentiment on a text or recognizing sentiment from a text. We propose a graph-based semi-supervised learning method to construct a sentiment dictionary as sentiment lexicon set. In particular, we focus on the construction of domain-specific sentiment dictionary. The proposed method makes up a graph according to lexicons and proximity among lexicons, and sentiments of some lexicons which already know their sentiment values are propagated throughout all of the lexicons on the graph. There are two typical types of the sentiment lexicon, sentiment words and sentiment phrase, and we construct a sentiment dictionary by creating each graph of them and infer sentiment of all sentiment lexicons. In order to verify our proposed method, we constructed a sentiment dictionary specific to the movie domain, and conducted sentiment classification experiments with it. As a result, it have been shown that the classification performance using the sentiment dictionary is better than the other using typical general-purpose sentiment dictionary.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.5
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• pp.267-278
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• 2017

As the same letter can be pronounced differently depending on word contexts, one should refer to a lexicon in order to pronounce a word correctly. Phonetic alphabets that lexicons adopt as well as pronunciations that lexicons describe for the same word can be different from lexicon to lexicon. In this paper, we use a sequence-to-sequence model that is widely used in deep learning research area in order to convert automatically from one pronunciation to another. The 12 seq2seq models are implemented based on pronunciation training data collected from 4 different lexicons. The exact accuracy of the models ranges from 74.5% to 89.6%. The aim of this study is the following two things. One is to comprehend a property of phonetic alphabets and pronunciations used in various lexicons. The other is to understand characteristics of seq2seq models by analyzing an error.

• Food Quality and Culture
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• v.2 no.1
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• pp.27-31
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• 2008

The objective of this study was to develop sensory lexicons that can be utilized for various types of tofu such as pressed, unpressed, and tofu made from germinated soybeans, using generic descriptive analysis. In the first phase of the experiment, trained descriptive panelists developed and defined the appearance, aroma, flavor, and texture attributes that are commonly present in tofu. Then, the sensory characteristics of seven types of tofu were analyzed using the sensory lexicons established in the initial stage of the experiment. Four appearance, 6 odor/aroma, 6 flavor/taste, 7 texture, and 4 aftertaste attributes were identified, and reference standards were established for most of the terms in order to facilitate the understanding of the attribute definitions. The intensities of the sensory attributes were measured on a 15-point scale. Statistical analyses, including analysis of variance and principal component analysis, were used for the data. The seven tofu samples showed significant differences in the intensities of 22 attributes. The unpressed tofu samples were generally rated as being high in moistness, easy to cut, silky, and easy to swallow. The pressed tofu, on the other hand, was salty, astringent, beany, hard, and rough in texture. The tofu made with germinated soybeans was characterized as having a strong cooked bean flavor, salty and astringent aftertaste, and hard texture. Overall, the attributes of moistness, easy to swallow, and silkiness showed strong positive correlations; hardness and sticks to teeth were also positively correlated to each other.

• Proceedings of the Korean Society for Language and Information Conference
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• 1992.02a
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• pp.99-110
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• 1992

• Korean Architects
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• s.653
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• pp.108-112
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• 2023

• Korean Architects
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• s.654
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• pp.102-106
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• 2023

• Korean Architects
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• s.652
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• pp.112-116
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• 2023

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.3
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• pp.300-306
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• 2013

This paper presents a new method for constructing bilingual lexicons through a pivot language. The proposed method is adapted from the context-based approach, called the standard approach, which is well-known for building bilingual lexicons using comparable corpora. The main difference between the standard approach and the proposed method is how to represent context vectors. The former is to represent context vectors in a target language, while the latter in a pivot language. The proposed method is very simplified from the standard approach thereby. Furthermore, the proposed method is more accurate than the standard approach because it uses parallel corpora instead of comparable corpora. The experiments are conducted on a language pair, Korean and Spanish. Our experimental results have shown that the proposed method is quite attractive where a parallel corpus directly between source and target languages are unavailable, but both source-pivot and pivot-target parallel corpora are available.