• 아동학회지
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• 제5권
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• pp.47-52
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• 1984

인지 어휘 유형(cognitive dictionary)이란 단어 연상의 반응 어휘를 인지 유형에 따라 분류, 분석하는 것을 가리킨다. 인지 어휘 유형 개념을 McNeill의 언어 발달 연구에 준하여 논의하였다. 즉 아동의 어의(語義) 발달은 자작문(自作文) 형식(形式) 표현에서 시작되어 어휘 사용에 이른다는 것이다. 한편 Moran은 범세계적으로 유아들의 인지 어휘 유형은 단어의 동작적(動作的) 특성에 주로 의거한다는 것을 발견했는데 이는 언어의 효시에 관한 Piaget 나 Bruner의 이론과 일치하는 것이다. Moran의 인지 어휘 유형의 추가 개념은 Bruner의 심상(心象)(ikonic representation)에 의한 관계, 기능적 관계 (functional representation), 논리적(logical)관계를 포함한 단어의 연합 관계에 반영시켰다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.240-248
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• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.295-297
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• 2007

This study suggests a computational model to inquire the roles of phonological information and orthography information in the process of visual word recognition among the courses of language information processing and the representation types of the mental lexicon. As the result of the study, the computational model showed the phonological and orthographic neighborhood effect among language phenomena which are shown in Korean word recognition, and showed proofs which implies that the mental lexicon is represented as phonological information in the process of Korean word recognition.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1391-1402
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• 2021

This paper focuses use context-sensitive spelling error correction using generative adversarial network. Generative adversarial network[1] are attracting attention as they solve data generation problems that have been a challenge in the field of deep learning. In this paper, sentences are generated using word embedding information and reflected in word distribution representation. We experiment with DCGAN[2] used for the stability of learning in the existing image processing and D2GAN[3] with double discriminator. In this paper, we experimented with how the composition of generative adversarial networks and the change of learning corpus influence the context-sensitive spelling error correction In the experiment, we correction the generated word embedding information and compare the performance with the actual word embedding information.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 춘계학술발표대회
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• pp.415-417
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• 2021

Since the words and words in Chinese sentences are continuous and the length of vocabulary is huge, Chinese NER(Named Entity Recognition) always based on character representation. In recent years, many Chinese research has been reconsidered how to integrate the word information into the Chinese NER model. However, the traditional sequence model has complex structure, the slow inference speed, and an additional dictionary information is needed, which is difficult to implement in the industry. The approach in this paper has the state of the art and parallelizable, which is integrated the char-word embeddings, so that the model learns word information. The proposed model is easy to implement, and outperforms traditional model in terms of speed and efficiency, which is improved f1-score on two dataset.

• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2002년도 춘계학술대회
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• pp.111-116
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• 2002

The regularity effect for printed word recognition and naming depends on ambiguities between single letters (small grain-size) and their phonemic values. As a given word is repeated and becomes more familiar, letter-aggregate size (grain-size) is predicted to increase, thereby decreasing the ambiguity between spelling pattern and phonological representation and, therefore, decreasing the regularity effect. Lexical decision and naming tasks studied the effect of repetition on the regularity effect for words. The familiarity of a word from was manipulated by presenting low and high frequency words as well as by presenting half the stimuli in mixed upper- and lowercase letters (an unfamiliar form) and half in uniform case. In lexical decision, the regularity effect was initially strong for low frequency words but became null after two presentations; in naming it was also initially strong but was merely reduced (although still substantial) after three repetitions. Mixed case words were recognized and named more slowly and tended to show stronger regularity effects. The results were consistent with the primary hypothesis that familiar word forms are read faster because they are processed at a larger grain-size, which requires fewer operations to achieve lexical selection. Results are discussed in terms of a neurobiological model of word recognition based on brain imaging studies.

• 한국문헌정보학회지
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• 제53권2호
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• pp.179-199
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• 2019

본 연구의 목적은 특허 문헌 분류에 가장 적합한 방법론을 발견하기 위하여 다양한 자질 추출 방법과 기계학습 및 딥러닝 모델을 살펴보고 실험을 통해 최적의 성능을 제공하는 방법론을 분석하는데 있다. 자질 추출 방법으로는 전통적인 BoW 방법과 분산표현 방식인 워드 임베딩 벡터를 비교 실험하고, 문헌 집합 구축 방식으로는 형태소 분석과 멀티그램을 이용하는 방식을 비교 검토하였다. 또한 전통적인 기계학습 모델과 딥러닝 모델을 이용하여 분류 성능을 검증하였다. 실험 결과, 분산표현 방법과 형태소 분석을 이용한 자질추출 방법을 기반으로 딥러닝 모델을 적용하였을 경우에 분류 성능이 가장 우수한 것으로 판명되었으며 섹션, 클래스, 서브클래스 분류 실험에서 전통적인 기계학습 방법에 비해 각각 5.71%, 18.84%, 21.53% 우수한 분류 성능을 보여주었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.290-291
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• 2008

The most popular speech feature used in speech recognition today is the Mel-Frequency Cepstral Coefficients (MFCC) algorithm, which could reflect the perception characteristics of the human ear more accurately than other parameters. This paper adopts MFCC and its first order difference, which could reflect the dynamic character of speech signal, as synthetical parametric representation. Furthermore, we quote Dynamic Time Warping (DTW) algorithm to search match paths in the pattern recognition process. We use the software "GoldWave" to record English digitals in the lab environments and the simulation results indicate the algorithm has higher recognition accuracy than others using LPCC, etc. as character parameters in the experiment for Digital Isolated Word Recognition (DIWR) system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2180-2197
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• 2019

With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.

• 정보과학회 논문지
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• 제44권7호
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• pp.742-747
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• 2017

본 논문에서는 문장의 분류에 있어 성능이 입증된 word2vec을 활용한 Convolutional Neural Network(CNN) 모델을 기반으로 하여 문서 분류에 적용 시 성능을 향상시키기 위해 doc2vec을 함께 CNN에 적용하고 기반 모델의 구조를 개선한 문서 분류 방안을 제안한다. 먼저 토큰화 방법을 선정하기 위한 초보적인 실험을 통하여, 어절 단위, 형태소 분석, Word Piece Model(WPM) 적용의 3가지 방법 중 WPM이 분류율 79.5%를 산출하여 문서 분류에 유용함을 실증적으로 확인하였다. 다음으로 WPM을 활용하여 생성한 단어 및 문서의 벡터 표현을 기반 모델과 제안 모델에 입력하여 범주 10개의 한국어 신문 기사 분류에 적용한 실험을 수행하였다. 실험 결과, 제안 모델이 분류율 89.88%를 산출하여 기반 모델의 분류율 86.89%보다 2.99% 향상되고 22.80%의 개선 효과를 보였다. 본 연구를 통하여, doc2vec이 동일한 범주에 속한 문서들에 대하여 유사한 문서 벡터 표현을 생성하기 때문에 문서의 분류에 doc2vec을 함께 활용하는 것이 효과적임을 검증하였다.