• Asia Marketing Journal
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• v.18 no.3
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• pp.1-18
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• 2016

The purpose of this paper is to examine empirically how to balance advertising expenditure before and after launch with regard to the direction of word of mouth in the motion picture industry. The vector auto-regression model is applied to assess the dynamic impact of advertising and word of mouth on sales. Empirical data, including advertising, word of mouth, and sales (the number of entries) of 83 movies are used for analysis. The research results show that for a movie having more positive word of mouth in the pre- and post-launch periods, it is worthwhile to spend the advertising budget in the pre-launch period only and to spare it in post-launch period. However, it is worthwhile to spare the advertising budget in the pre-launch period for movies having less positive word of mouth before and after launch, and to concentrate spending in post-launch period instead. Mangers who handle products and services facing shortened lifecycles, such as games, eBooks, and digital music contents, need to check the quality of pre-launch word of mouth for their advertising budget decisions in the pre- and post-launch periods and spend more of the advertising budget in the post- (pre-) launch period if pre-launch word of mouth is negative (positive). For products and services with a shortened lifecycle, it is recommended to spend more of the advertising budget in the post- (pre-) launch period if pre-launch word of mouth is negative (positive).

• Education of Primary School Mathematics
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• v.15 no.2
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• pp.107-134
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• 2012

The purpose of this study is to investigate the effectiveness of two teaching methods of word problems, one based on mathematical modeling learning(ML) and the other on traditional learning(TL). Additionally, the influence of mathematical modeling learning in word problem solving behavior, application ability of real world experiences in word problem solving and the beliefs of word problem solving will be examined. The results of this study were as follows: First, as to word problem solving behavior, there was a significant difference between the two groups. This mean that the ML was effective for word problem solving behavior. Second, all of the students in the ML group and the TL group had a strong tendency to exclude real world knowledge and sense-making when solving word problems during the pre-test. but A significant difference appeared between the two groups during post-test. classroom culture improvement efforts. Third, mathematical modeling learning(ML) was effective for improvement of traditional beliefs about word problems. Fourth, mathematical modeling learning(ML) exerted more influence on mathematically strong and average students and a positive effect to mathematically weak students. High and average-level students tended to benefit from mathematical modeling learning(ML) more than their low-level peers. This difference was caused by less involvement from low-level students in group assignments and whole-class discussions. While using the mathematical modeling learning method, elementary students were able to build various models about problem situations, justify, and elaborate models by discussions and comparisons from each other. This proves that elementary students could participate in mathematical modeling activities via word problems, it results form the use of more authentic tasks, small group activities and whole-class discussions, exclusion of teacher's direct intervention, and classroom culture improvement efforts. The conclusions drawn from the results obtained in this study are as follows: First, mathematical modeling learning(ML) can become an effective method, guiding word problem solving behavior from the direct translation approach(DTA) based on numbers and key words without understanding about problem situations to the meaningful based approach(MBA) building rich models for problem situations. Second, mathematical modeling learning(ML) will contribute attitudes considering real world situations in solving word problems. Mathematical modeling activities for word problems can help elementary students to understand relations between word problems and the real world. It will be also help them to develop the ability to look at the real world mathematically. Third, mathematical modeling learning(ML) will contribute to the development of positive beliefs for mathematics and word problem solving. Word problem teaching focused on just mathematical operations can't develop proper beliefs for mathematics and word problem solving. Mathematical modeling learning(ML) for word problems provide elementary students the opportunity to understand the real world mathematically, and it increases students' modeling abilities. Futhermore, it is a very useful method of reforming the current problems of word problem teaching and learning. Therefore, word problems in school mathematics should be replaced by more authentic ones and modeling activities should be introduced early in elementary school eduction, which would help change the perceptions about word problem teaching.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.679-687
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• 2020

The present study aims to investigate lexical coverage of BNC (British National Corpus) word lists and 2015 Basic Vocabulary of Ministry of Education in 2020 EBS-linked textbooks and CSAT. For the data analysis, AntWordProfiler was used to find lexical coverage and frequency. The findings showed that Students can understand 95% of the tokens with a vocabulary of BNC 3,000 and 4,000 word-families in 2020 EBS-linked listening and reading books respectively. 98% can be understood with 4,000 word-families in the EBS-linked listening book while the same lexical coverage can be covered with 8,000 word-families in the EBS-linked reading textbook. By the way, 95% of the tokens can be understood with 2,000 and 4,000 word-families in 2020 CSAT listening and reading tests respectively, while 98% requires 4,000 and 7,000 word-families in the 2020 listening and reading tests respectively. In summary, students should understand more words in 2020 EBS-linked textbooks than in 2020 CSAT tests confirming Kim's (2016) findings. In summary, students should understand more words in 2020 EBS-linked textbooks than in 2020 CSAT tests.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.97-103
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• 2001

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. Although the ML method has good performance, it dose not take account into discrimination to other words. To complement this problem, a word verification method by re-recognition of the recognized word and its similar word using the discriminative function of the two words. To find the similar word, the probability of other words to the HMM is calculated and the word showing the highest probability is selected as the similar word of the mode. To achieve discrimination to each word the weight to each state is appended to the HMM parameter. The weight is calculated by genetic algorithm. The verificator complemented discrimination of each word and reduced the error occurred by similar word. As a result of verification the total error is reduced by about 22%

• Korean Journal of Child Studies
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• v.14 no.2
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• pp.79-94
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• 1993

The purpose of this study was (1) to investigate whether linguistic contrast helps children map a new word into a specific semantic domain when a new word is introduced, (2) to examine the existence of a hierarchy of domains into which children will place a new word, (3) to examine whether children's existing lexicons affect how children map a new word. A total of 320 children from 3 to 6 years of age were drawn from Pusan, Korea. The children were divided into one of four age groups. There were 80 children in each age group. In each group, children were randomly assigned to one of four groups; the linguistic contrast group exposed to color, the linguistic contrast group exposed to shape, a label group and control group. All of the children were tested for production and comprehension of the new word. The results of this study were as follows; (1) The linguistic contrast helped children learn the meanings of a new word. Especially, children age 4 or more showed a significant effect for linguistic contrast; however, it was not sufficient to teach 3-year-old the correct, referent of a term. (2) There was a hierarchy of domains into which children mapped a new word. There was no significant effect for domains into which 3-year-old children mapped the new word, but from 4 years of age children showed a preference for assuming a new word refered to an object's shape rather than its color. (3) Children's existing lexicon had no effect, on how children comprehend a new word.

• Korean Journal of Cognitive Science
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• v.17 no.1
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• pp.15-27
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• 2006

The effects of the number of Hangul syllable, the nunber/location of batchim in a Hangul word, and compound/noncompound Hangul word on memory span were examined. The results were that (1) the more syllables a word had, the lower us memory span was, (2) the more batchims a two-syllable word had, the lower its memory span was (Korean batchim effect on memory span), (3) noncompound word had higher memory span than compound word. The reading speed of above mentioned words was measured and the results were that (1) the more syllables a word had, the slower its reading speed was, (2) but the reading speed of a two-syllable word was forest when it had a batchim on second syllable than when it had no batchim or had a batchim on first syllable or batchims on both syllables (Korean ending batchim effect on reading speed), (3) noncompound word was read faster thu compound word. Korean ending batchim effect on reading speed was not compatible with the explanation by articulatory loop bur compatible with the explanation by visual cache where the orthographic information was represented. The results suggest that memory span was influenced nor only by phonological information but also by orthographic information.

• Journal of Elementary Mathematics Education in Korea
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• v.13 no.2
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• pp.247-268
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• 2009

The purpose of this study was to examine the mathematical components of word problems and the structure of the components, to examine the characteristics of the understanding of mathematics high achievers about word problems, and ultimately to devise a teaching method geared toward facilitating learner understanding of the word problems. Given the findings of the study, the following conclusion was reached: First, word problems could be categorized according to their mathematical components, namely the mathematical structure of multiple variables provided to learners for their problem solving. And learner's reaction might hinge on the type of word problems. Second, the mathematics high achievers relied on diverse strategies to understand the mathematical components of word problems to solve the problems. The use of diverse strategies made it possible for them to succeed in problem solving. Third, identifying the characteristics of the understanding of the mathematics high achievers about word problems made it possible to layout successful lesson plans that stressed understanding of the mathematical structure of word problems. And the teaching plans enabled the learners to get a better understanding of the given word problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1300-1306
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• 2018

The purpose of this study is to see possibility of Char2Vec as alternative of Word2Vec that most famous word embedding model in Sentence Similarity Measure Problem by Deep-Learning. In experiment, we used the Siamese Ma-LSTM recurrent neural network architecture for measure similarity two random sentences. Siamese Ma-LSTM model was implemented with tensorflow. We train each model with 200 epoch on gpu environment and it took about 20 hours. Then we compared Word2Vec based model training result with Char2Vec based model training result. as a result, model of based with Char2Vec that initialized random weight record 75.1% validation dataset accuracy and model of based with Word2Vec that pretrained with 3 million words and phrase record 71.6% validation dataset accuracy. so Char2Vec is suitable alternate of Word2Vec to optimize high system memory requirements problem.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.85-94
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• 2009

The purpose of this study was to see the effects of pitch and length patterns on spoken word recognition. In Experiment 1, a syllable monitoring task was used to see the effects of pitch and length on the pre-lexical level of spoken word recognition. For both Seoul dialect speakers and Daegu dialect speakers, pitch and length did not affect the syllable detection processes. This result implies that there is little effect of pitch and length in pre-lexical processing. In Experiment 2, a lexical decision task was used to see the effect of pitch and length on the lexical access level of spoken word recognition. In this experiment, word frequency (low and high) as well as pitch and length was manipulated. The results showed that pitch and length information did not play an important role for Seoul dialect speakers, but that it did affect lexical decision processing for Daegu dialect speakers. Pitch and length seem to affect lexical access during the word recognition process of Daegu dialect speakers.