• Journal of Korean Library and Information Science Society
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• v.12
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• pp.199-232
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• 1985

Word processor having the functions of a typewriter and a computer together have been used as a powerful tools for office automation. The purpose of this study is to find out the possible areas of the word processing a n.0, pplication in the library operations. For the study, the general concept, developmental process, structure, functions, kinds and suggested a n.0, pplicable areas in the library operations of word processor were investigated. Then, the cases of real a n.0, pplications of word processor in the library field were examplified. In conclusion, the areas where word processor can be of benefit to library workers can be summarized as follows, 1) Orders of books and periodical acquisitions, 2) On-line searching, and storage and editing of input as required, 3) Production of catalogues, and abstracting and indexing bulletin, 4) Budget control, circulation control, and serial control.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.136-146
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• 1995

In this paper, a Japanese morphological analyzer for Japanese to Korean Machine Translation System is designed. The analyzer reconstructs the Japanese input sentence into word phrases that include grammatical and dictionary informations. Thus we propose the algorithm to separate morphemes and then connect them by reference to a corresponding Korean word phrases. And we define the connector to control Japanese word phrases It is used in controlling the start and the end point of the word phrase in the Japanese sentence which is without a space. The proposed analyzer uses the analysis dictionary to perform more efficient analysis than the existing analyzer. And we can decrease the number of its dictionary searches. Since the analyzer, proposed in this paper, for Japanese to Korean Machine Translation System processes each word phrase in consideration of the corresponding Korean word phrase, it can generate more accurate Korean expressions than the existing one which places great importance on the generation of the entire sentence structure.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.165-168
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• 2000

First, we give a linear-time algorithm to find the first overlap in an arbitrary binary word. Second, we implement the algorithm in the C language and show that the number of comparisons in this algorithm is less than 31n, where n$\geq$3 is the length of the input word.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.63-75
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• 1998

Since many co-articulation problems are occurring in continuous spoken Korean language, several researches use words as a basic recognition unit. Though the word unit can solve this problem, it requires much memory and has difficulty fitting an input speech in a word list. In this paper, we propose an hidden Markov model(HMM) based recognition model that is an interconnection network of word HMMs for a syntax of sentences. To match suitably the input sentence into the continuous word list in the network, we use a level building search algorithm. This system represents the large sentence set with a relatively small memory and also has good extensibility. The experimental result of an airplane reservation system shows that it is proper method for a practical recognition system.

• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.82-91
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• 1991

The history of automatic speech recognition research, and current and future speech products at Fujitsu are introduced here. The speech recognition research at Fujitsu started in 1970. Our research efforts have results in the production of a speaker dependent 12,000 word discrete / connected word recognizer(F2360), and a speaker independent 17 word discrete word recognizer(F2355L/S). Currently, we are working on a larger vocabulary speech recognizer, in which an input utterance will be matched with networks representing possible phonemic variations. Its application to text input is also discussed.

• The Korean Journal of Community Living Science
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• v.17 no.3
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• pp.3-14
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• 2006

The purpose of this study was to investigate the relationship between conversational function and question type in mothers' utterances and their infant's language development. The subjects were 20 infants from 1;07 to 1;11 years of age in Yanji, China. Each child's spontaneous natural speech during interaction with his/her mother was videotaped for about 30 minutes. The children and their mother's spontaneous utterances were transcribed and coded for the number of type and token of word, grammatical morpheme conversational function and type of question in mother's language input to her child. The result showed that mothers used questions as the most frequent conversational function with their infants. The number of questions in conversational function in mothers' utterances positively correlated with the type of word, type of morpheme and grammatical morpheme in infants' utterance. However, there was no correlation between mothers' language input and infant early language development.

• ETRI Journal
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• v.45 no.1
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• pp.93-104
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• 2023

Coreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks-based coreference resolution for Korean using multi-task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head-final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word- and sentence-level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre-trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.3-14
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• 2011

It has been posited that in English, native listeners use the Metrical Segmentation Strategy (MSS) for the segmentation of continuous speech. Strong syllables tend to be perceived as potential word onsets for English native speakers, which is due to the high proportion of strong syllables word-initially in the English vocabulary. This study investigates whether Koreans employ the same strategy when segmenting speech input in English. Word-spotting experiments were conducted using vowel-initial and consonant-initial bisyllabic targets embedded in nonsense trisyllables in Experiment 1 and 2, respectively. The effect of strong syllable was significant in the RT (reaction times) analysis but not in the error analysis. In both experiments, Korean listeners detected words more slowly when the word-initial syllable is strong (stressed) than when it is weak (unstressed). However, the error analysis showed that there was no effect of initial stress in Experiment 1 and in the item (F2) analysis in Experiment 2. Only the subject (F1) analysis in Experiment 2 showed that the participants made more errors when the word starts with a strong syllable. These findings suggest that Koran listeners do not use the Metrical Segmentation Strategy for segmenting English speech. They do not treat strong syllables as word beginnings, but rather have difficulties recognizing words when the word starts with a strong syllable. These results are discussed in terms of intonational properties of Korean prosodic phrases which are found to serve as lexical segmentation cues in the Korean language.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.254-276
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• 2019

Since the amount of information on the internet is growing rapidly, it is not easy for a user to find relevant information for his/her query. To tackle this issue, the researchers are paying much attention to Document Summarization. The key point in any successful document summarizer is a good document representation. The traditional approaches based on word overlapping mostly fail to produce that kind of representation. Word embedding has shown good performance allowing words to match on a semantic level. Naively concatenating word embeddings makes common words dominant which in turn diminish the representation quality. In this paper, we employ word embeddings to improve the weighting schemes for calculating the Latent Semantic Analysis input matrix. Two embedding-based weighting schemes are proposed and then combined to calculate the values of this matrix. They are modified versions of the augment weight and the entropy frequency that combine the strength of traditional weighting schemes and word embedding. The proposed approach is evaluated on three English datasets, DUC 2002, DUC 2004 and Multilingual 2015 Single-document Summarization. Experimental results on the three datasets show that the proposed model achieved competitive performance compared to the state-of-the-art leading to a conclusion that it provides a better document representation and a better document summary as a result.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.04a
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• pp.18-28
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• 1983

This paper describes the result of a series of fundamental researches on a word processing system, named WORD80. Given the text consisting of Ilanguel characters, Chinese characters, English alphabets and other special characters, it can provide-ㄴo-see printout, after having processed according to the commands used to control the output format of the text. Since each Hanguel character in input text is made up of and is represented as some compoents, it uses the deterministic finite automata for the formation of Hanguel from its components. When developed at first, the WORD80 can perform Hanguel to Chinese transition character by character by character. But it was thought to be more practical to do this word by word.