• Annual Conference on Human and Language Technology
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• 2007.10a
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• pp.189-193
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• 2007

In this paper, we designed a methodology to expand the WordNet. We added unknown terms like IT technical terms to the existing WordNet by using web search. The WordNet is an online taxonomy representing the relationships among terms, but it usually showed limitation to contain new technical terminologies. That's why we tried to expand the WordNet. Firstly, when we met unregistered terms in WordNet, we built a query of those terms for web search. Given a web search results, we tried to find out terms with a high-level relatedness with the unregistered terms. We used the Korean Morphological Analyzer to score the relatedness between terms and located the unregistered term as a hyponym of terms with high score of relatedness.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.189-194
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• 2016

본 연구는 다국어 감성사전 및 감성주석 코퍼스 구축 프로젝트인 MUSE 프로젝트의 일환으로 한국어 감성사전을 구축하기 위해 대표적인 영어 감성사전인 SentiWordNet을 이용하여 한국어 감성사전을 구축하는 방법의 의의와 한계점을 검토하는 것을 목적으로 한다. 우선 영어 SentiWordNet의 117,659개의 어휘중에서 긍정/부정 0.5 스코어 이상의 어휘를 추출하여 구글 번역기를 이용해 자동 번역하는 작업을 실시하였다. 그 중에서 번역이 되지 않거나, 중복되는 경우를 제거하고, 언어학 전문가들의 수작업으로 분류해 낸 결과 3,665개의 감성어휘를 획득할 수 있었다. 그러나 이마저도 병명이나 순수 감성어휘로 보기 어려운 사례들이 상당수 포함되어 있어 실제 이를 코퍼스에 적용하여 감성어휘를 자동 판별했을 때에 맛집 코퍼스에서의 재현율(recall)이 긍정과 부정에서 각각 47.4%, 37.7%, IT 코퍼스에서 각각 55.2%, 32.4%에 불과하였다. 이와 더불어 F-measure의 경우, 맛집 코퍼스에서는 긍정과 부정의 값이 각각 62.3%, 38.5%였고, IT 코퍼스에서는 각각 65.5%, 44.6%의 낮은 수치를 보여주고 있어, SentiWordNet 기반의 감성사전은 감성사전으로서의 역할을 수행하기에 충분하지 않은 것으로 나타났다. 이를 통해 한국어 감성사전을 구축할 때에는 한국어의 언어적 속성을 고려한 체계적인 접근이 필요함을 역설하고, 현재 한국어 전자사전 DECO에 기반을 두어 보완 확장중인 SELEX 감성사전에 대해 소개한다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.149-161
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• 2013

In this paper, we propose a novel approach to construction of classification hierarchies for user tags of folksonomies, using WordNet-based Formal Concept Analysis tool, called TagLighter, which is developed on this research. Finally, to give evidence of the usefulness of this approach in practice, we describe some experiments on user tag data of Bibsonomy.org site. The classification hierarchies of user tags constructed by our approach allow us to gain a better and further understanding and insight in tagged data during information retrieval and data analysis on the folksonomy-based systems. We expect that the proposed approach can be used in the fields of web data mining for folksonomy-based web services, social networking systems and semantic web applications.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.131-138
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• 2013

In this paper, I propose and evaluate the method that classifies emotional type of characters with their emotional words. Emotional types are classified as three types such as positive, negative and neutral. They are selected by classification of emotional words that characters speak. I propose the method to extract emotional words based on WordNet, and to represent as emotional vector. WordNet is thesaurus of network structure connected by hypernym, hyponym, synonym, antonym, and so on. Emotion word is extracted by calculating its emotional distance to each emotional category. The number of emotional category is 30. Therefore, emotional vector has 30 levels. When all emotional vectors of some character are accumulated, her/his emotion of a movie can be represented as a emotional vector. Also, thirty emotional categories can be classified as three elements of positive, negative, and neutral. As a result, emotion of some character can be represented by values of three elements. The proposed method was evaluated for 12 characters of four movies. Result of evaluation showed the accuracy of 75%.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.454-460
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• 2020

Artificial intelligence assistants that provide speech recognition operate through cloud-based voice recognition with high accuracy. In cloud-based speech recognition, Wake-Up-Word (WUW) detection plays an important role in activating devices on standby. In this paper, we compare the performance of Convolutional Neural Network (CNN)-based WUW detection models for mobile devices by using Google's speech commands dataset, using the spectrogram and mel-frequency cepstral coefficient features as inputs. The CNN models used in this paper are multi-layer perceptron, general convolutional neural network, VGG16, VGG19, ResNet50, ResNet101, ResNet152, MobileNet. We also propose network that reduces the model size to 1/25 while maintaining the performance of MobileNet is also proposed.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1101-1112
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• 2004

In this paper, we propose a Web page weighting scheme using the user preference in each sense of query word to improve the performance of Web search. Generally search engines assign weights to a web page by using relevancy only, which is obtained by comparing the query word and the words in a web page. In the information retrieval from huge data such as the Web, simple word comparison cannot distinguish important documents because there exist too many documents with similar relevancy In this paper we implement a WordNet-based user interface that helps to distinguish different senses of query word, and constructed a search engine in which the implicit evaluations by multiple users are reflected in ranking by accumulating the number of clicks. In accumulating click counts, they are stored separately according to senses, so that more accurate search is possible. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7718-7728
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• 2015

Semantic similarity between words can be applied in many fields including computational linguistics, artificial intelligence, and information retrieval. In this paper, we present weighted method for measuring a semantic similarity between words in a document. This method uses edge distance and depth of WordNet. The method calculates a semantic similarity between words on the basis of document information. Document information uses word term frequencies(TF) and word concept frequencies(CF). Each word weight value is calculated by TF and CF in the document. The method includes the edge distance between words, the depth of subsumer, and the word weight in the document. We compared out scheme with the other method by experiments. As the result, the proposed method outperforms other similarity measures. In the document, the word weight value is calculated by the proposed method. Other methods which based simple shortest distance or depth had difficult to represent the information or merge informations. This paper considered shortest distance, depth and information of words in the document, and also improved the performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.317-324
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• 2013

Twitter sentiment analysis is to classify a tweet (message) into positive and negative sentiment class. This study deals with SentiWordNet(SWN)-based twitter sentiment analysis. SWN is a sentiment dictionary in which each sense of an English word has a positive and negative sentimental strength. There has been a variety of SWN-based sentiment feature extraction methods which typically first determine the sentiment orientation (SO) of a term in a document and then decide SO of the document from such terms' SO values. For example, for SO of a term, some calculated the maximum or average of sentiment scores of its senses, and others computed the average of the difference of positive and negative sentiment scores. For SO of a document, many researchers employ the maximum or average of terms' SO values. In addition, the above procedure may be applied to the whole set (adjective, adverb, noun, and verb) of parts-of-speech or its subset. This work provides a comparative study on SWN-based sentiment feature extraction schemes with performance evaluation on a well-known twitter dataset.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.568-570
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• 2004

기존의 컬러 기반 감성시소러스 구축에서는 감성 어휘 관계 생성을 위해 IRI에서 개발한 단색이미지 스케일을 사용하여 컬러를 표현하였다. 그리고 컬러에 따라 연상되는 형용사를 단색 이미지 스케일에 배치하여 형용사 이미지 스케일의 공간관계를 생성하였다 즉, 컬러를 보고 연상되는 이미지를 형용사로 표현하고 색의 고유한 관계를 공간상에 배치하여 컬러 값에 따른 형용사를 매핑하였다. 이는 단순히 컬러의 관계만을 이용한 설정으로 의미적 감성어 표현에는 한계가 있었다. 이에 본 논문에서는 HP사의 'The Meaning of Color'표를 이용하여 컬러에 따른 감성 이미지를 정의하고, 정의된 어휘간의 의미적 표현을 위해 유의어, 동의어 관계 등을 포함한 WordNet 구조를 기반으로 새로운 감성어 관계 생성 방안을 제안한다. 방안을 제안한다.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.28-32
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• 2002

웹 상의 많은 정보들 속에서 사용자가 원하는 정보를 찾아내는 일은 쉽지 않다. 사용자가 의도하는 양질의 정보 제공을 위해 특정 영역과 관련한 정보 제공 시스템이 .개발되고 있다. 이전 시스템은 특정 영역 관련 지식베이스를 토대로 하여 웹 문서를 수집해 놓고, 사용자에게 정보를 제공한다. 본 논문에서는 전문 사이트 내에 문서간의 유사성을 토대로 하여 동물 영역에 대한 효과적인 문서 클러스타링(clustering)에 관해 실험하였다. 기존의 방법에서는 문서의 분류나 질의어와 관련한 문서 선택이나 순위 결정이 주로 텀(term)을 바탕으로 하고 있다. 본 논문에서는 각 문서 내의 텀 뿐만 아니라 HTML 태그(tag), 지식베이스에 WordNet의 계층구조를 적용한 data를 활용하고, SVD(Singular Value Decomposition)를 사용하여 문서간의 관계를 밝혀내어 문서 분류 및 수집에 이용하였다. 특정 영역의 전문 문서를 많이 제공하는 사이트에 적용하여 좋은 결과를 볼 수 있었다.