• 전자통신동향분석
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• 제34권4호
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• pp.55-64
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• 2019

In this study, we introduce trends in neural-network-based deep learning research applied to dialogue systems. Recently, end-to-end trainable goal-oriented dialogue systems using long short-term memory, sequence-to-sequence models, among others, have been studied to overcome the difficulties of domain adaptation and error recognition and recovery in traditional pipeline goal-oriented dialogue systems. In addition, some research has been conducted on applying reinforcement learning to end-to-end trainable goal-oriented dialogue systems to learn dialogue strategies that do not appear in training corpora. Recent neural network models for end-to-end trainable chit-chat systems have been improved using dialogue context as well as personal and topic information to produce a more natural human conversation. Unlike previous studies that have applied different approaches to goal-oriented dialogue systems and chit-chat systems respectively, recent studies have attempted to apply end-to-end trainable approaches based on deep neural networks in common to them. Acquiring dialogue corpora for training is now necessary. Therefore, future research will focus on easily and cheaply acquiring dialogue corpora and training with small annotated dialogue corpora and/or large raw dialogues.

• 한국임상수의학회지
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• 제16권1호
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• pp.118-127
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• 1999

The aim of this study was to establish the method of differential diagnosis between developing and regressing corpus luteum in cows. Plasma progesterone (P$_4$) concentrations were determined by radioimmunoassay in slaughtered, cycling and pregnant cows. Ultrasonography was used to measure the corpus luteum size and histogram values for determining the correlationships between corpus luteum area or histogram values and plasma P$_4$ concentrations. The corpora lutea were monitored in vitro (water-bath scanning) by using ultrasonography with 7.5 MHz linear-array transducer in 196 slaughtered cows. The correlation coefficient between corpus luteum area and plasma P$_4$ concentrations was 0.46 (p<0.01), and between histogram values and plasma P$_4$ concentrations was -0.44 (p<0.01), respectively. The corpora lutea were monitored by ultrasonography with 5.0 MHz linear-array transrectal transducer in 188 cycling and 30 pregnant cows. The corpus luteum areas and plasma P4 concentrations were significantly different between regressing and other corpora lutea (p<0.01), and also histogram values were significantly different between regressing and developing corpola lutea (p＜0.01). The correlation coefficients between corpus luteum areas and plasma P$_4$ concentrations were 0.76 (p<0.01), 0.71 (p<0.01), 0.65 (p＜0.05) and 0.68 (p<0.05), and between histogram values and plasma P$_4$ concentrations were 0.74 (p<0.05), 0.71 (p＜0.01), -0.52 (p<0.05) and 0.65 (p＜0.05) in developing, functional, regressing and pregnant corpora lutea, respectively. These results indicate that corpus luteum areas and plasma P$_4$ concentrations were highly correlated in all stages of corpus luteum. The histogram values and plasma P$_4$ concentrations were positive correlated in developing, functional and pregnant corpora lutea, but negative correlated in regressing corpus luteum. Therefore, the measurement of corpus luteum area and histogram value by ultrasonography is reliable method for the assessment of luteal function, specially developing and regressing corpus luteum.

• 영어어문교육
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• 제17권2호
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• pp.1-18
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• 2011

The object of this study is to show some features of English, focused on such mechanics as punctuation and transition, in Korean presidential addresses transcribed in English which are different from those of the United States. Towards that end, the presidential addresses of the United States and Korea from January, 2010 to June, 2010 are collected, made into corpora, and analyzed. Through analyzing the corpora, this paper is to address the following research questions: (1) What features can be regarded as different in terms of punctuation and transition? (2) If there are any differences between the corpora, are they significant enough to pose any problems for Korean and American English users to communicate with each other? (3) If so, what can be done to solve the problems in regard to pedagogical implications? Overall, as for punctuation, both Presidents' addresses share a lot in common, even with some idiosyncratic variations though. However, there are some noticeable differences in transitional devices. It is not clear whether those should be taken as a sign of personal preference, though. Transitional markers are meant to be part of wording in writing. (196 words).

• 한국콘텐츠학회논문지
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• 제4권4호
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• pp.1-10
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• 2004

음성인식의 연구 대상은 낭독음성에서 대화음성으로 발전해가고 있다. 이를 위해서는 대량의 대화코퍼스가 필요하다. 그러나 아직 충분한 양의 대화코퍼스가 구축되어 있지 못하며 코퍼스의 주석 정보 또한 복잡하고 다양하게 표현하고 있어 효율적인 활용이 어렵다. 따라서 본 논문에서는 TEI를 기반으로 하여 대화 영역을 텔레뱅킹으로 설정하고 대화코퍼스를 구축하여 구축된 대화코퍼스의 주석 정보를 XML(extensible Markup Language)로 표준화할 수 있도록 DTD (Document Type Definition) 정의하고 저장 시스템을 설계하였다.

• 한국언어정보학회지:언어와정보
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• 제14권1호
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• pp.145-163
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• 2010

This paper aims to propose that Benford's Law, non-uniform distribution of the leading digits in lists of numbers from many real-life sources, also appears in linguistic texts. The first digits in the frequency lists of morphemes from Sejong Morphologically Analyzed Corpora represent non-uniform distribution following Benford's Law, but showing complexity of numerical sources from complex systems like earthquakes. Benford's Law in texts is a principle reflecting regular distribution of low-frequency linguistic types, called LNRE(large number of rare events), and governing texts, corpora, or sample texts relatively independent of text sizes and the number of types. Although texts share a similar distribution pattern by Benford's Law, we can investigate non-uniform distribution slightly varied from text to text that provides useful applications to evaluate randomness of texts distribution focused on low-frequency types.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.146-154
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• 2007

AutoCor is a method for the automatic acquisition and classification of corpora of documents in closely-related languages. It is an extension and enhancement of CorpusBuilder, a system that automatically builds specific minority language corpora from a closed corpus, since some Tagalog documents retrieved by CorpusBuilder are actually documents in other closely-related Philippine languages. AutoCor used the query generation method odds ratio, and introduced the concept of common word pruning to differentiate between documents of closely-related Philippine languages and Tagalog. The performance of the system using with and without pruning are compared, and common word pruning was found to improve the precision of the system.

• 정보과학회 논문지
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• 제44권2호
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• pp.163-170
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• 2017

통계적 기계 번역을 이용한 구어-수화 번역 연구가 활발해짐에도 불구하고 수화 말뭉치의 자원 희소성 문제는 해결되지 않고 있다. 본 연구는 수화 번역의 첫 번째 단계로써 통계적 기계 번역을 이용한 구어-수지 신호 번역에서 말뭉치 자원 희소성으로부터 기인하는 문제점들을 해결할 수 있는 세 가지 전처리 방법을 제안한다. 본 연구에서 제안하는 방법은 1) 구어 문장의 패러프레이징을 통한 말뭉치 확장 방법, 2) 구어 단어의 표제어화를 통한 개별 어휘 출현 빈도 증가 및 구어 표현의 번역 가능성을 향상시키는 방법, 그리고 3) 수지 표현으로 전사되지 않는 구어의 기능어 제거를 통한 구어-수지 표현 간 문장 성분을 일치시키는 방법이다. 서로 다른 특징을 지닌 영어-미국 수화 병렬 말뭉치들을 이용한 실험에서 각 방법론들이 단독으로 쓰일 때와 조합되어 함께 사용되었을 때 모두 말뭉치의 종류와 관계없이 번역 성능을 개선시킬 수 있다는 것을 확인할 수 있었다.

• 한국지능시스템학회논문지
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• 제14권3호
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• pp.357-362
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• 2004

언어 모델은 음성 인식이나 필기체 문자 인식 등에서 다음 단어를 예측함으로써 인식률을 높이게 된다. 그러나 언어 모델은 그 도메인에 따라 모두 다르며 충분한 분량의 말뭉치를 수집하는 것이 거의 불가능하다. 본 논문에서는 N그램 방식의 언어모델을 구축함에 있어서 크기가 제한적인 말뭉치의 한계를 극복하기 위하여 두개의 말뭉치, 즉 소규모의 구어체 말뭉치와 대규모의 문어체 말뭉치의 통계를 이용하는 방법을 제시한다. 이 이론을 검증하기 위하여 수십만 단어 규모의 방송용 말뭉치에 수백만 이상의 신문 말뭉치를 결합하여 방송 스크립트에 대한 퍼플렉시티를 30% 향상시킨 결과를 획득하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제39권2호
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• pp.172-178
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• 2015

Constructing a bilingual multi-word lexicon is confronted with many difficulties such as an absence of a commonly accepted gold-standard dataset. Besides, in fact, there is no everybody's definition of what a multi-word unit is. In considering these problems, this paper evaluates and analyzes the context vector approach which is one of a novel alignment method of constructing bilingual lexicons from parallel corpora, by comparing with one of general methods. The approach builds context vectors for both source and target single-word units from two parallel corpora. To adapt the approach to multi-word units, we identify all multi-word candidates (namely noun phrases in this work) first, and then concatenate them into single-word units. As a result, therefore, we can use the context vector approach to satisfy our need for multi-word units. In our experimental results, the context vector approach has shown stronger performance over the other approach. The contribution of the paper is analyzing the various types of errors for the experimental results. For the future works, we will study the similarity measure that not only covers a multi-word unit itself but also covers its constituents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.