• 말소리와 음성과학
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• 제7권4호
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• pp.3-8
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• 2015

In this paper, we investigate an input dimension reduction method using continuous word vector in deep neural network language model. In the proposed method, continuous word vectors were generated by using Google's Word2Vec from a large training corpus to satisfy distributional hypothesis. 1-of-${\left|V\right|}$ coding discrete word vectors were replaced with their corresponding continuous word vectors. In our implementation, the input dimension was successfully reduced from 20,000 to 600 when a tri-gram language model is used with a vocabulary of 20,000 words. The total amount of time in training was reduced from 30 days to 14 days for Wall Street Journal training corpus (corpus length: 37M words).

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권11호
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• pp.1359-1371
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• 1999

대규모 네트워크 상에서 동작하는 분산 시스템의 구현을 위해 제시된 방법 중의 하나인 이동 코드 개념은 네트워크 공유 자원에 접근할 수 있는 효과적인 방법을 제시하였고 이 개념을 지원하는 많은 언어들의 개발을 가져왔다. 개발된 언어들이 가지고 있는 이동 코드를 지원하기 위한 언어 구문과 적용하려는 문제 영역의 특성을 반영한 언어 구문은 네트워크 프로그래밍을 하는데 있어서 효율과 문제 중심의 프로그램의 두 가지를 모두 가능하게 하고 있다. 본 논문에서는 현재 분산 컴퓨팅 환경에서 가장 많이 사용되고 있는 클라이언트-서버 모델을 확장하여 서버의 자원에 접근할 수 있는 또 다른 방법을 가진 모델을 제시하고, 이 모델을 표현할 수 있는 언어를 설계하였다. 설계된 언어는 이동 코드의 개념을 지원함으로써 대규모 네트워크에서 수행되는 프로그램의 작성을 가능하게 하고, 분산 범위 규칙을 채택함으로써 이동 코드의 기술을 일반 함수를 기술하듯 명확한 관점에서 할 수 있도록 하였다. 또한 네트워크 관련 자원들을 언어 구문으로 채택하여 네트워크 프로그래밍을 언어 수준에서 할 수 있도록 하였다. 언어의 이론적인 설계에 그치지 않고 설계된 언어를 수행할 수 있는 실행 시간 지원 시스템을 구현하였다. 실행 시간 지원 시스템은 언어를 해석하고 실행하는 코드 해석기와 이동 코드를 지원하는 네트워크 감독으로 구성되며 설계된 언어를 사용하여 실제로 네트워크 응용 프로그램을 작성하고 테스트 해 볼 수 있다.Abstract Some studies bring up a concept of code mobility as an innovative way to access network resources in order to develop distributed systems working on a large scale network. After that, many languages are suggested to support this concept. In these languages, language constructors for their particular application domains and mobile codes provide both problem-oriented views to the programmer and reasonable performance to the system. In this thesis, we extend the client-server model that is the most popular model in developing distributed systems these days. We propose a model to have another method to access server's resources and extend the C language to implement the proposed model for the large scale network. The new language has capability to build a software working on a large scale network by supporting mobile code and gives a consistent network programming view to the programmer by adapting distributed semantics. The language also makes network programming easy by providing network primitives at the language level. We implement a prototype of run-time system to support this language. The run-time system is composed of two major parts: code-interpreter that interprets and executes the language and network-daemon that supports mobile codes.

• ETRI Journal
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• 제43권6호
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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.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 1998년도 Language, Information and Computation = Selected Papers from the 12th Pacific Asia Conference on Language, Information and Computation, Singapore
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• pp.139-151
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• 1998

• Journal of Information Processing Systems
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• 제18권4호
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• 한국영어학회지:영어학
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• 제3권4호
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• pp.527-545
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• 2003

I look at the respective responsibilities of different components of the language faculty in the description of two radically different kinds of linguistic phenomenon. The first is the production/perception mismatch in the child's acquisition of the phonology of its first language. There is strong evidence that the child's lexical representations are the same as the adult's, but I argue that the child's own pronunciations, have no linguistic status and are best treated as the product of a neural network. The second is the nature of compositionality, where I argue that compositionality in Natural Language is derivative from that in the Language of Thought. With this assumption and using evidence from quantification in ‘backward control’ structures, I argue that chain theory is intrinsically inimical to a simple view of the legibility relation between LF and LoT.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.685-688
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• 2018

의존 구문 분석은 자연어 문장에 포함된 단어들 간의 의존 관계를 분석하는 과제로 다양한 자연어 이해 과제에 요구되는 핵심 기술 중 하나이다. 본 연구에서는 단어와 문자 자질을 적용한 기존 Stack-Pointer Network의 인코더의 입력 단어 표상을 확장하여, 한국어를 비롯한 형태적으로 복잡한 언어(morphologically rich language)에 적합하도록 음절-태그 단위, 형태소 단위, 형태소 품사 정보 자질을 보강한 의존 구문 분석 모델을 제안한다. 실험 결과 제안하는 모델은 의존 구조로 변환된 세종 구문 분석 말뭉치에서 UAS 90.58%, LAS 88.35%의 성능을, 2018 국어 정보 처리 시스템 경진 대회 평가 데이터에서 UAS 84.69%, LAS 82.02%의 성능을 보였다. 더불어 제안하는 모델은 포함된 문장의 전체 길이가 긴 의존 관계, 의존소와 지배소의 거리가 먼 의존 관계, 의존소를 구성하는 형태소의 개수가 많은 의존 관계에서 기존 Stack-Pointer Network보다 향상된 성능을 보였다.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.195-200
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• 2023

The main purpose of the article is to study the peculiarities of the work of the Ukrainian language in the upper grades of the lyceum based on the activity approach. Despite the fact that a number of scientific studies and applied developments on teaching Ukrainian as a foreign language have recently appeared in Ukrainian linguistics, significant problems in this area should be recognized (organization of the educational process when learning a language as a foreign language, general methodological principles, psycho- and sociolinguistic foundations, communicative approaches), the non-resolution of which leads to methodologically unreasonable teaching of the Ukrainian language as a foreign language, the use of methods of teaching the language as a native language or the study of the language as a subject (linguistic aspect). In addition, due attention is not paid to the development of communication skills, which, firstly, worsens the quality of teaching and learning. Based on the results of the analysis, the key aspects of the work on the Ukrainian language in the senior classes of the lyceum were analyzed on the basis of an activity approach.

• ETRI Journal
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• 제40권4호
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• pp.435-445
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• 2018

This paper proposes a human-like sign-language learning method that uses a deep-learning technique. Inspired by the fact that humans can learn sign language from just a set of pictures in a book, in the proposed method, the input data are pre-processed into an image. In addition, the network is partially pre-trained to imitate the preliminarily obtained knowledge of humans. The learning process is implemented with a well-known network, that is, a convolutional neural network. Twelve sign actions are learned in 10 situations, and can be recognized with an accuracy of 99% in scenarios with low-cost equipment and limited data. The results show that the system is highly practical, as well as accurate and robust.

• International Journal of Contents
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• 제12권2호
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• pp.66-74
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• 2016

In recent years, studies have emerged highlighting the role of social networking services in the English as a foreign language context of Korea, particularly for teaching and learning, but none examine the role of Facebook in enhancing communities of practice. Therefore, this study undertakes such an endeavor on a Korea-based language teacher organization Facebook group. Social networking analysis came to reveal a group consisting of largely non-engaged members, with several key users successfully bridging the network and promoting engagement and interaction via commenting. Although the most viral posts were disproportionately organizational, professional development clearly emerges as the largest concern for active members. Ultimately, it will be critical for increasing success of the group as a social networking service to establish a means of further engaging all members, including those on the network periphery. To fully meet organizational goals, it will be imperative for key actors to be used increasingly proactively.