• 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.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2E
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• pp.27-31
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• 1997

This paper proposes a new rejection algorithm which distinguishes unregistered spoken words(or non-keywords) from registered vocabulary. Two kinds of garbage models are employed in this design ; the original garbage model and a new word garbage model. The original garbage model collects all non-keyword patterns where the new word garbage model collects patterns classified by recognizing each non-keyword pattern with registered vocabulary. These two types of garbage models work together to make a robust reject decision. The first stage of processing is the classification of an input pattern through the original garbage model. In the event that the first stage of processing is ambiguous, the new word dependent garbage model is used to classify thye input pattern as either a registered or non-registered word. This paper shows the efficiency of the new word dependent garbage model. A Dynamic Multisection method is used to test the performance of the algorithm. Results of this experiment show that the proposed algorithm performs at a higher level than that of the original garbage model.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.263-266
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• 2019

단어 임베딩 모델 중 현재 널리 사용되는 word2vec 모델은 언어의 의미론적 유사성을 잘 반영한다고 알려져 있다. 본 논문은 word2vec 모델로 학습된 단어 벡터가 실제로 의미론적 유사성을 얼마나 잘 반영하는지 확인하는 것을 목표로 한다. 즉, 유사한 범주의 단어들이 벡터 공간상에 가까이 임베딩되는지 그리고 서로 구별되는 범주의 단어들이 뚜렷이 구분되어 임베딩되는지를 확인하는 것이다. 간단한 군집화 알고리즘을 통한 검증의 결과, 상식적인 언어 지식과 달리 특정 범주의 단어들은 임베딩된 벡터 공간에서 뚜렷이 구분되지 않음을 확인했다. 결론적으로, 단어 벡터들의 유사도가 항상 해당 단어들의 의미론적 유사도를 의미하지는 않는다. Word2vec 모델의 결과를 응용하는 향후 연구에서는 이런 한계점에 고려가 요청된다.

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.11-28
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• 2016

In this paper, we proposed a dictionary extension and a ternary decomposition technique to improve the effectiveness of Khmer word segmentation. Most word segmentation approaches depend on a dictionary. However, the dictionary being used is not fully reliable and cannot cover all the words of the Khmer language. This causes an issue of unknown words or out-of-vocabulary words. Our approach is to extend the original dictionary to be more reliable with new words. In addition, we use ternary decomposition for the segmentation process. In this research, we also introduced the invisible space of the Khmer Unicode (char\u200B) in order to segment our training corpus. With our segmentation algorithm, based on ternary decomposition and invisible space, we can extract new words from our training text and then input the new words into the dictionary. We used an extended wordlist and a segmentation algorithm regardless of the invisible space to test an unannotated text. Our results remarkably outperformed other approaches. We have achieved 88.8%, 91.8% and 90.6% rates of precision, recall and F-measurement.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.43-49
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• 1992

As a result of recent progress in speech synthesis techniques, the those new services using new techniques are going to introduce into the telephone communication system. In setting standards, voice quality is obviously an important criterion. It is very important to develope a quality evaluation method of synthesized speech for the diagnostic assessment of system algorithm, and fair comparison of assessment values. This paper has described several basic concepts and criterions for quality assessment (intelligibility) of synthesized speech by rule, and then a word selection method and the word list to be used in word intelligibility test were proposed. Finally, a test method for word intelligibility is described.

• Cross-Cultural Studies
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• v.33
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• pp.359-384
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• 2013

There is linguistic phenomenon that Korean syllable, morpheme and word are substituted with Chinese Character. These phenomenon is called Chinese Character Borrowing, the Chinese character used here is called Borrowed Chinese Character. Whereas borrowing Chinese character in Chinese is used for borrowing only sound for different word, borrowing Chinese character in Korean is used for assigning new meaning. Hence, by borrowing Chinese character in Korean, a syllable which had no meaning originally get new meaning, morpheme and word meaning has changed. At advertisement and campaign, Chinese Character Borrowing has lots of linguistical advantage such as visual immediacy, effectiveness of meaning expression. However, there are number of cases found that violate grammar rule and word constitution practice by Chinese Character Borrowing. For this reason, Chinese Character Borrowing has the problem polluting Korean along with another foreign words. Thus, this paper focus on study Chinese Character Borrowing phenomenon in Korean, and analysis its effectiveness and impact in Korean. In addition, analysis the problem of Borrowed chinese Character, and suggestion several alternative for right use of Korean is followed.

• 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 the Korea Society of Computer and Information
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• v.21 no.7
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• pp.9-16
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• 2016

Collaborative filtering(CF) uses the purchase or item rating history of other users, but does not need additional properties or attributes of users and items. Hence CF is known th be the most successful recommendation technology. But conventional CF approach has some significant weakness, such as the new user problem. In this paper, we propose a approach using word embedding with skip-gram for learning distributed item representations. In particular, we show that this approach can be used to capture precise item for solving the "new user problem." The proposed approach has been tested on the Movielens databases. We compare the performance of the user based CF, item based CF and our approach by observing the change of recommendation results according to the different number of item rating information. The experimental results shows the improvement in our approach in measuring the precision applied to new user problem situations.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.241-252
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• 2016

The purpose of this study is to explain the word-of-mouth communication that evolve differently from traditional marketing communications using existing mass media in an environment of the flooding of new products. In particular, people use digital devices as internet users and various communication is activated in blogs and communities. It is expanding that companies use market maven as a facilitator of communication. The role of Market maven due to the spread of the Internet seems to be more meaningful for marketing activities. Market maven is a look at how it should study moderate effect on word of mouth of new products. Empirical results show that perceived curiosity and perceived innovativeness of new product has a positive impact on the word of mouth of new product, and was confirmed in a moderate effect of market maven.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.31-35
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• 1997

In this paper, we propose a variable vocabulary word recognizer that is able to recognize new words not exist in training data. For the variable vocabulary word recognizer, we must have an on-line lexicon generator to transform new candidate words to the corresponding pronunciation sequences of phones without any large lexicon table. And, we also must make outputs. In order to model the phones and allophones reliably, we define Korean allophones by triphone clustering based on phonetic knowledge of preceding and succeeding phones of each phone. Using the clustering method, we generated 1,548 allophones with POW (Phonetically Optimized Words) 3,848 word DB. We evaluated the proposed word recognizer with POW 3,848 DB, PBW (Phonetically Balanced Words) 445 DB, and 244 word DB in hotel reservation task. Experimental results showed word recognition accuracy of 79.6% for the POW DB corresponding to vocabulary-dependent case, 79.4% in case of 445 word lexicon and 88.9% in case of 100 word lexicon for the PBW DB, and 71.4% for the hotel reservation DB corresponding to vocabulary-independent case.