• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.141-151
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• 2021

This study analyzed the meaning and functions of '-(으)ㄹ 것 같다' and '-나 보다' among the various semantic functions depending on the situation, and discussed ways to train speculative expressions more efficiently by expanding them from traditional teaching methods through visualizations applied visual thinking at real Korean language education. The speculative representation, which is the subject of this study, represents the speaker's speculation about something or situation, with slight differences in meaning depending on the basis of the speculation and the subject of the speculation. We propose a training method that can enhance the diversification and efficiency of teaching-learning through visualization of information or knowledge, speculative representations that exhibit fine semantic differences in various situations. Utilizing visual thinking in language education can simplify and provide language information through visualization of language knowledge, and learners can be efficient at organizing and organizing language knowledge. It also has the advantage of long-term memory of language information through visualization of language knowledge. Attempts of various educational methods that can be applied at the Korean language education site can contribute to establishing a more systematic and efficient education method, which is meaningful in that the visual thinking proposed in this study can give interest and efficiency to international students.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.41-60
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• 1991

Most of the PC users manipulate the computer by using a few commands which are familiar with them. However by using Hangul sentences instead of using DOS commands, the optimal commands can be generated and flexibility can be provided. For this purpose, the conversion method of the input sentence into DOS commands is studied by means of morphological analysis, syntactic analysis, semantic analysis, and conceptual analysis. Tabular parsing is used in morphological analysis. case grammar is used in syntactic and semantic analysis. Case grammar is used in syntactic and semantic analysis. The meaning of sentence is represeented by the semantic network, from which we can generate a sequence DOS commands.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.993-997
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• 2009

Clustering is well known method and that can be used in statistical machine translation. In this paper we propose a corpus clustering method using syntactic structure and POS information of dependency grammar. And using this cluster language model as additional feature to phrased-based statistical machine translation system to improve translation Quality.

• English Language & Literature Teaching
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• v.11 no.3
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• pp.1-23
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• 2005

This paper is a qualitative and quantitative study. The main purpose of the paper is to diagnose what makes English communication difficult at the Korean EFL adult level. In order to obtain data, this study employed interviews and a questionnaire. We identified thirty three factors blocking pathways to oral communication. Qualitative analysis repeatedly revealed patterns such as lack of grammar, lack of vocabulary, lack of background knowledge, and peer pressure, but quantitative analysis yielded somewhat different results: lack of colloquial expressions, lack of vocabulary, lack of various topics, problems in the educational system, difficulty in using existing knowledge, and lack of grammar. Findings which were common to both qualitative and quantitative analyses suggest that lack of linguistic knowledge and lack of background knowledge are major barriers learners encounter in communication. On the basis of the results, suggestions are made for overcoming these barriers.

• English Language & Literature Teaching
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• v.15 no.4
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• pp.199-216
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• 2009

This study investigates how Korean speakers develop their interlanguage of English passive constructions with a reference to the learners' grammar proficiency levels. Sixty two college students of different levels of English participated in this study. They were asked to complete a sentence-completion task. Their production was classified into accurate passives, malformed passives, pseudo-passives, unaccusatives, and actives according to the use of transitive, ergative and unergative verbs. They then were further analyzed depending on the subjects' levels of grammar by three main factors: L1 transfer, the English voice system, and universal cognitive factors. The results showed that the subjects of the lower group produced more pseudo-passives, malformed passives, and overpassivization than those of the higher group, and even subjects of higher group still made passives for ergative verbs. It was also shown that L1 and universal factors had more influence on the lower group than on the higher group. Based on the analyses of the subjects' responses, the development of the English passive system by Korean learners is shown and some implications are suggested for effective teaching of English.

• ETRI Journal
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• v.44 no.3
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.27-32
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• 2008

Sign languages, commonly used in aurally challenged communities, are a kind of visual language expressing sign words with motion. Spatiality and motility of a sign language are conveyed mainly via sign words as predicates. A predicate is modified by an adverbial phrase with an accompanying change in its semantics so that the adverbial phrase can also affect the overall spatiality and motility of expressions of a sign language. In this paper, we analyze the semantic features of adverbial phrases which may affect the motion-related semantics of a predicate in converting expressions in Korean into those in a sign language and propose a system that generates corresponding animation by utilizing these features.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.2-27
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• 2002

As pact of a long-ranged project that aims at establishing database-theoretic semantics as a model of computational semantics, this presentation focuses on the development of a syntactic component for processing strings of words or sentences to construct semantic data structures. For design arid modeling purposes, the present treatment will be restricted to the analysis of some problematic constructions of Korean involving semi-free word order, conjunction arid temporal anchoring, and adnominal modification and antecedent binding. The present work heavily relies on Hausser's (1999, 2000) SLIM theory for language that is based on surface compositionality, time-linearity arid two other conditions on natural language processing. Time-linear syntax for natural language has been shown to be conceptually simple and computationally efficient. The associated semantics is complex, however, because it must deal with situated language involving interactive multi-agents. Nevertheless, by processing input word strings in a time-linear mode, the syntax cart incrementally construct the necessary semantic structures for relevant queries and valid inferences. The fragment of Korean syntax will be implemented in Malaga, a C-type implementation language that was enriched for both programming and debugging purposes arid that was particluarly made suitable for implementing in Left-Associative Grammar. This presentation will show how the system of syntactic rules with constraining subrules processes Korean sentences in a step-by-step time-linear manner to incrementally construct semantic data structures that mainly specify relations with their argument, temporal, and binding structures.

• Annual Conference on Human and Language Technology
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• 1993.10a
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• pp.559-570
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• 1993

In this article we present a rapid development of a Korean to English translation system, by the help of general English generator, PENMAN. PENMAN is an English sentence generation system, of which input language is a language specially devised for sentence generation, named Sentence Planning Language(SPL). The language SPL has various features that are necessary for generating sentences, covering both syntactic and semantic features. In this development we integrated a Korean language parser based on dependency grammar and the English sentence generator PENMAN, bridging two systems through a converting module, which converts dependency structures produced by Korean parser into SPL for PENMAN.

• The Journal of the Acoustical Society of Korea
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• v.19 no.6
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• pp.62-70
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• 2000

In usual language models(LMs), the probability has been estimated by selecting highly frequent words from a large text side database. However, in case of adopting LMs in a specific task, it is unnecessary to using the general method; constructing it from a large size tent, considering the various kinds of cost. In this paper, we propose a construction method of LMs using a small size text database in order to be used in specific tasks. The proposed method is efficient in increasing the low frequent words by applying same sentences iteratively, for it will robust the occurrence probability of words as well. We carried out continuous speech recognition(CSR) experiments on 200 sentences uttered by 3 speakers using LMs by iterative teaming(IL) in a air flight reservation task. The results indicated that the performance of CSR, using an IL applied LMs, shows an 20.4% increased recognition accuracy compared to those without it. This system, using the IL method, also shows an average of 13.4% higher recognition accuracy than the previous one, which uses context-free grammar(CFG), implying the effectiveness of it.