• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• 전기전자학회논문지
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• 제12권4호
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• pp.217-224
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• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.

• 정보과학회 논문지
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• 제43권7호
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• pp.773-780
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• 2016

기계학습 기반의 의미역 인식에서 어휘, 구문 정보가 자질로 주로 쓰이지만, 의미 정보를 분석하는 의미역 인식은 의미 정보 또한 매우 유용한 정보이다. 그러나, 기존 연구에서는 의미 정보를 활용할 수 있는 방법이 제한되어 있기 때문에, 소수의 연구만 진행되었다. 본 논문에서는 의미 정보를 활용하는 방안으로 동형이의어 수준의 의미 애매성 해소 기술, 고유 명사에 대한 개체명 인식 기술, 의미 정보에 기반한 필터링, 유의어 사전을 이용한 클러스터 및 기존 의미 프레임 정보 확장, 구문-의미 정보 연동 규칙, 필수 의미역 오류 보정 등을 제안한다. 제안하는 방법은 기존 연구 대비 뉴스 도메인인 Korean Propbank는 3.77, 위키피디아 문서 기반의 Exobrain GS 3.0 평가셋에서는 8.05의 성능 향상을 보였다.

• ETRI Journal
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• 제32권3호
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• pp.482-485
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• 2010

Feature selection is very important for feature-based relation classification tasks. While most of the existing works on feature selection rely on linguistic information acquired using parsers, this letter proposes new features, including probabilistic and semantic relatedness features, to manifest the relatedness between patterns and certain relation types in an explicit way. The impact of each feature set is evaluated using both a chi-square estimator and a performance evaluation. The experiments show that the impact of relatedness features is superior to existing well-known linguistic features, and the contribution of relatedness features cannot be substituted using other normally used linguistic feature sets.

• 대한전자공학회논문지SP
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• 제48권5호
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• pp.23-29
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• 2011

적조에 의한 양식업 및 수산업의 피해가 증가함에 따라서 적조에 대한 많은 연구가 이루어지고 있다. 그러나 자동으로 적조 이미지를 인식하는 국내의 연구는 미흡한 실정이다. 적조 생물은 이미지 객체를 일치 할 수 있는 기준 중심 특징이 없기 때문에 인식이 어렵다. 이 때문에 기존이 연구들은 단순히 몇 종류의 적조 생물만을 이미지 분류에 이용하고 있다. 본 논문은 비음수 행렬 분해의 의미 특징과 이미지 객체의 원형율을 이용한 새로운 적조 이미지 인식 방법을 제안한다.

• International Journal of Internet, Broadcasting and Communication
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• 제8권4호
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• pp.63-72
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• 2016

Nominal compounds of 'N1 + N2'in Korean can be classified into the following three major categories: co-compound, sub-compound, and fusion. Among these three major categories, insertion of /t/ in the compounding process and subsequent tensification are found only in sub-compounds. This peculiar phenomenon of /t/-insertion which causes, in turn, tensification in sub-compounds has been long controversial because linguists have not been able to expect in which phonological environment of sub-compounding insertion of /t/ takes place. In this paper, I explore a phonological rule which makes it possible to expect the phonological environments of sub-compounding that allow insertion of /t/ and automatic tensification of the subsequent consonant in the onset of N2. In this process, I show that semantic feature(s) between two combined roots should be considered as one of the important structural descriptions in phonology.

• ETRI Journal
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• 제29권5호
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• pp.545-558
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• 2007

This paper presents an efficient semantic service discovery scheme called UbiSearch for a large-scale ubiquitous computing environment. A semantic service discovery network in the semantic vector space is proposed where services that are semantically close to each other are mapped to nearby positions so that the similar services are registered in a cluster of resolvers. Using this mapping technique, the search space for a query is efficiently confined within a minimized cluster region while maintaining high accuracy in comparison to the centralized scheme. The proposed semantic service discovery network provides a number of novel features to evenly distribute service indexes to the resolvers and reduce the number of resolvers to visit. Our simulation study shows that UbiSearch provides good semantic searchability as compared to the centralized indexing system. At the same time, it supports scalable semantic queries with low communication overhead, balanced load distribution among resolvers for service registration and query processing, and personalized semantic matching.

• 한국콘텐츠학회논문지
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• 제7권1호
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• pp.1-9
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• 2007

의미 포토 검색은 포토의 간단한 시각화 특성과 적합한 의미를 분류하는데 있어서의 갭을 간소화시키는 데 중요한 역할을 한다. 의미 검색을 이용한 효과적인 포토 검색은 포토 검색에 있어서 매우 중요한 과제중의 하나이다. 따라서 우리는 사용자 인터페이스의 포토 주석을 이용한 새로운 이벤트 의미 포토 검색 기법을 제안한다. 본 논문에서는 순수 XML 데이터베이스와 MPEG-7표준을 기반으로 포토 관리 및 의미 검색이 쉬운 포토 앨범 관리 시스템을 설계 및 구현하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3790-3803
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• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

• Journal of information and communication convergence engineering
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• 제5권3호
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• pp.249-253
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• 2007

The Research on the ICT(Information & Communication Technology) is proposed the category to IT839 strategy by Government. Government is driving to researching on technology about IT839 Strategy. By transforming this category and research information into Semantic Web environment, it is possible to search function utilizing knowledge base and information object by use of TBox and ABox. In this regard, this study proposes technology for generation of Semantic Web Document about ICT Research Information. The ontology is constructed by using category to IT839 Strategy. The features of framework proposed in this study is to have used a skill to directly map Ontology instance and in case of inability of direct mapping, proposed a skill to establish reliable Semantic Web Document by suggesting indirect mapping skill using mechanical study. In addition, it is possible to establish low cost/high quality Semantic Web Document about ICT research information.