• The Journal of the Acoustical Society of Korea
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• v.24 no.1E
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• pp.28-33
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• 2005

This study was designed to suggest an assessment tool for analyzing the characteristics of Korean phonetic contrast intelligibility among dysarthric individuals. The intelligibility deficit factors of phonetic contrast in Korean dysarthric patients were analyzed through stepwise regression analysis. The 19 acoustic-phonetic contrasts proposed by Kent et al. (1999) have been claimed to be useful for clinical assessment and research on dysarthria. However, the test cannot be directly applied to Korean patients due to linguistic differences between English and Korean. Thus, it is necessary to devise a Korean word intelligibility test that reflects the distinct characteristics of the Korean language. To identify the speech error characteristics of a Korean dysarthric group, a Korean word list was audio-recorded by 3 spastic, 4 flaccid, and 5 mixed type of dysarthric patients. The word list consisted of monosyllabic consonant-vowel-consonant (CVC) real word pairs. Stimulus words included 41 phonemic contrast pairs and six triplets. The results showed that the percentage of errors in final position contrast was higher than in any other position. Unlike the results of previous studies, the initial-position contrasts were crucial in predicting the overall intelligibility among Korean patients.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.694-702
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• 2003

The word intelligibility test for dysarthric speakers was designed to examine phonetic contrasts that are likely (1) to be sensitive to intelligibility impairment and (2) to contribute significantly to speech intelligibility. These phonetically contrasting word pairs were tested and proved to be reliable and to be valid, The results showed that in Korean dysarthric patients, the percentage of error in final position contrast was higher than in any other position. Unlike the results of previous studies, the initial-position contrasts were crucial in predicting the overall intelligibility among Korean patients.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.833-844
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• 2023

As technology convergence is recognized as a driver of innovation, the identification of technology convergence opportunities is critical to expanding a firm's technology portfolio. Recently, wearable technology has emerged as an important factor in creating new business opportunities and providing technology investment alternatives for firms in the era of Industry 4.0. Against this background, this study provides a new patent analysis framework for identifying and proposing technology convergence opportunities in the wearable field. Using 8,621 patents filed between 2011 and 2021, a case study was conducted to identify technological convergence opportunities by applying Word2Vec algorithm. The analysis framework can be divided into four stages, with the final stage recommending potential technology convergence opportunities for a specific candidate firm's technology area by calculating similarities between technology codes. This study aims to better understand the current status of wearable technology development as well as to propose a new methodology for capturing technology convergence opportunities in the wearable industry. The case study result suggests that the convergence of healthcare and ICT may provide new development opportunities. Furthermore, the results are expected to provide alternative perspectives on the development of new markets and technologies using wearable technology and can support the strategic decision-making on future technology planning in the wearable field.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.830-841
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• 2023

Tokenization is the process of segmenting the input text into smaller units of text, and it is a preprocessing task that is mainly performed to improve the efficiency of the machine learning process. Various tokenization methods have been proposed for application in the field of natural language processing, but studies have primarily focused on efficiently segmenting text. Few studies have been conducted on the Korean language to explore what tokenization methods are suitable for document classification task. In this paper, an exploratory study was performed to find the most suitable tokenization method to improve the performance of a representative spatio-temporal document classifier in Korean. For the experiment, a convolutional neural network model was used, and for the final performance comparison, tasks were selected for document classification where performance largely depends on the tokenization method. As a tokenization method for comparative experiments, commonly used Jamo, Character, and Word units were adopted. As a result of the experiment, it was confirmed that the tokenization of word units showed excellent performance in the case of representative spatio-temporal document classification task where the semantic embedding ability of the token itself is important.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1400-1418
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• 2020

In the development of commercial promotion, chatbot is known as one of significant skill by application of natural language processing (NLP). Conventional design methods are using bag-of-words model (BOW) alone based on Google database and other online corpus. For one thing, in the bag-of-words model, the vectors are Irrelevant to one another. Even though this method is friendly to discrete features, it is not conducive to the machine to understand continuous statements due to the loss of the connection between words in the encoded word vector. For other thing, existing methods are used to test in state-of-the-art online corpus but it is hard to apply in real applications such as telemarketing data. In this paper, we propose an improved chatbot design way using hybrid bag-of-words model and skip-gram model based on the real telemarketing data. Specifically, we first collect the real data in the telemarketing field and perform data cleaning and data classification on the constructed corpus. Second, the word representation is adopted hybrid bag-of-words model and skip-gram model. The skip-gram model maps synonyms in the vicinity of vector space. The correlation between words is expressed, so the amount of information contained in the word vector is increased, making up for the shortcomings caused by using bag-of-words model alone. Third, we use the term frequency-inverse document frequency (TF-IDF) weighting method to improve the weight of key words, then output the final word expression. At last, the answer is produced using hybrid retrieval model and generate model. The retrieval model can accurately answer questions in the field. The generate model can supplement the question of answering the open domain, in which the answer to the final reply is completed by long-short term memory (LSTM) training and prediction. Experimental results show which the hybrid word vector expression model can improve the accuracy of the response and the whole system can communicate with humans.

• The Journal of the Acoustical Society of Korea
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• v.19 no.6
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• pp.28-33
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• 2000

This paper investigates the durational difference between syllable final segment and syllable initial one within word level. The Korean consonant (m) and (nn) were focused mainly. It could hardly say that there was significant difference between preceding consonant and following one, but it was observed that the preceding consonant tended to be shorter than the following one in the (mm) case. This might be explained by the fact that bilabial sound should appear at the first step of language acquisition. This leads to the conclusion that the articulation of preceding (m) shall be easier than others. In the case of alveolar geminate (nn), there was considerable statistic difference between preceding and following segments. It tends to be that the preceding consonant has longer duration.

• Fashion & Textile Research Journal
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• v.18 no.3
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• pp.317-326
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• 2016

The main purposes of this study were to segment Chinese consumers who purchase fashion products through internet commerce according to internet shopping values, to compare their online word-of-mouth acceptance and dissemination behavior, and to examine the demographic characteristics and purchase behavior of the segments. 715 questionnaires were collected through internet survey from January $19^{th}$ to March $16^{th}$, 2015 and a total of 488 were used for the final data analysis. The respondents were twenty to thirty nine years old men and women living in all over China. Hedonic and utilitarian shopping values were identified through factor analysis and based on the shopping values, the respondents were categorized into four groups-ambivalent shopping value group, hedonic shopping value group, utilitarian shopping value group and indifferent group. Among these groups, there were significant differences in terms of online word-of-mouth acceptance as well as dissemination level and motivation. In overall, ambivalent shopping value group showed high online word-of-mouth acceptance as well as dissemination motivation. The groups also showed significant differences in clothing selection criteria, frequently purchased internet shopping sites, online clothing shopping frequency and information sources. The groups also differed in terms of age, residential area, education level, occupation and income. However, there were no significant differences in gender and marital status among the groups.

• Language and Information
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• v.14 no.1
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• pp.113-144
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• 2010

The primary goal of this paper is to find a feasible way to answer the question: Does the similarity in meaning between verbs relate to the similarity in their subcategorization? In order to answer this question in a rather concrete way on the basis of a large set of English verbs, this study made use of various language resources, tools, and statistical methodologies. We first compiled a list of 678 verbs that were selected from the most and second most frequent word lists from the Colins Cobuild English Dictionary, which also appeared in WordNet 3.0. We calculated similarity measures between all the pairs of the words based on the 'jcn' algorithm (Jiang and Conrath, 1997) implemented in the WordNet::Similarity module (Pedersen, Patwardhan, and Michelizzi, 2004). The clustering process followed, first building similarity matrices out of the similarity measure values, next drawing dendrograms on the basis of the matricies, then finally getting 177 meaningful clusters (covering 437 verbs) that passed a certain level set by z-score. The subcategorization frames and their frequency values were taken from the ICE-GB. In order to calculate the Selectional Preference Strength (SPS) of the relationship between a verb and its subcategorizations, we relied on the Kullback-Leibler Divergence model (Resnik, 1996). The SPS values of the verbs in the same cluster were compared with each other, which served to give the statistical values that indicate how much the SPS values overlap between the subcategorization frames of the verbs. Our final analysis shows that the degree of overlap, or the relationship between semantic similarity and the subcategorization frames of the verbs in English, is equally spread out from the 'very strongly related' to the 'very weakly related'. Some semantically similar verbs share a lot in terms of their subcategorization frames, and some others indicate an average degree of strength in the relationship, while the others, though still semantically similar, tend to share little in their subcategorization frames.

• ETRI Journal
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• v.43 no.6
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• pp.1038-1048
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• 2021

We propose an end-to-end neural coreference resolution for the Korean language that uses an attention mechanism to point to the same entity. Because Korean is a head-final language, we focused on a method that uses a pointer network based on the head. The key idea is to consider all nouns in the document as candidates based on the head-final characteristics of the Korean language and learn distributions over the referenced entity positions for each noun. Given the recent success of applications using bidirectional encoder representation from transformer (BERT) in natural language-processing tasks, we employed BERT in the proposed model to create word representations based on contextual information. The experimental results indicated that the proposed model achieved state-of-the-art performance in Korean language coreference resolution.

• Korean Journal of Cognitive Science
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• v.1 no.2
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• pp.221-239
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• 1989

The purpose of this study was to explore the processing unit in Korean word.Three experiments were conducted to examine this question.Preliminary experiment and Enperiment I were executed to delineate the processing unit in singles syllable word and Experiment 2,for words two or more syllables.The major finding of the preliminary experiment showed that the effect of the consonant type was not significant but that of the letter position was.Reaction time increased as the position of letter increased.The difference in reaction time between the first and the second position was not significant.However,the difference between the second and third was.In the Experiment 1, the effect of the number of letter was significant: reaction time increased as the number of letters increased.The size of the position effect both in the preliminary experiment and Experiment 1was comparable.Result of Experiment 2 was such that regardless of the presence of the final consonant(s),the reaction time incresased linearly as the number of svllables increased from two to four. The findings of the present study suggest that:(1)processing unit in single syllable Korean words is a syllable without the final consonant(s):(2) but in words of two or more syllables,the unit is likely to be a syllable with the final consonant(s).