• 한국경영과학회지
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• 제15권2호
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• pp.1-15
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• 1990

A mathematical definition of the cluster is suggested. A nonlinear 0-1 integer programming formulation for the multi-dimensional entity clustering problem is developed. A heuristic method named MDEC (Multi-Dimensional Entity Clustering) using centroids and the binary partition is developed and the numerical examples are shown. This method has an advantage of providing bottle-neck entity informations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1148-1152
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• 1987

This paper analyzes the statistical and scientific entity-aspect model for statistical and scientific databases(SSDB's). The statistical and scientific entity-aspect model(SEAM) is defined an example of the application of the statistical and scientific entity-aspect model is represented. Finally, the statistical and scientific entity-aspect model as a design tool for SSDB is evaluated and the further research areas are suggested.

• Journal of Computing Science and Engineering
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• 제8권3호
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• pp.137-148
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• 2014

Named entity classification of Wikipedia articles is a fundamental research area that can be used to automatically build large-scale corpora of named entity recognition or to support other entity processing, such as entity linking, as auxiliary tasks. This paper describes a method of classifying named entities in Chinese Wikipedia with fine-grained types. We considered multi-faceted information in Chinese Wikipedia to construct four feature sets, designed different feature selection methods for each feature, and fused different features with a vector space using different strategies. Experimental results show that the explored feature sets and their combination can effectively improve the performance of named entity classification.

• International Journal of Contents
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• 제18권1호
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• pp.56-64
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• 2022

To improve the cooperation ability of emergency entities, in this research, the emergency activities model, skill contribution degree, entity-relationship strength, activity continuity, and emergency entity cooperation degree were defined. Emergency entity cooperation degree and emergency activity continuity table were constructed with emergency cases, emergency plans, and emergency drill plans, and factors were further excavated which affected them. In this paper, we focus on the factors which affect the cooperation ability of emergency entities, the relationship between the emergency cooperation ability and the number of cooperation, entity-relationship intensity, emergency activity frequency, and skill contribution of fire entities and medical entities were obtained. These data results are of great significance to decision-makers in formulating emergency rescue and emergency plans.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.218-221
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• 1996

전사적 데이터 모델을 개발하기 위해서는, 먼저, 기업에 있어서 중요하게 관리되어져야 할 주요 entity들을 파악하는 것이 선행되어야 한다. entity의 결정은 시스템 개발 전 단계에 걸쳐 지대한 영향을 끼치는 중요한 의사결정이나, 아직까지 이는 매우 주관적일 뿐 아니라 의사결정자의 경험 및 전문성에 매우 의존적이다. 또한 때로는 entity의 결정에 필요 이상의 많은 시간이 소요되기도 한다. 본 연구에서는 entity결정에 직면한 의사결정자를 지원하기 위하여, 사례기반 추론 기술을 채택한 의사결정지원시스템을 설계 개발하였다. 본 시스템에서는 과거에 성공적으로 entity를 결정했었다고 평가되는 사례로부터, 해당 기업의 상황에 적합한 새로운 결론을 도출해서 의사결정자를 효과적으로 지원한다.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2021년도 제33회 한글 및 한국어 정보처리 학술대회
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• pp.482-485
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• 2021

최근 지식 그래프를 확장하기 위해 많은 연구가 진행되고 있다. 지식 그래프를 확장하기 위해서는 relation을 기준으로 entity의 방향성을 고려하는 것이 매우 중요하다. 지식 그래프를 확장하기 위한 대표적인 연구인 관계 추출은 문장과 2개의 entity가 주어졌을 때 relation을 예측한다. 최근 사전학습 언어모델을 적용하여 관계 추출에서 높은 성능을 보이고 있지만, entity에 대한 방향성을 고려하여 relation을 예측하는지 알 수 없다. 본 논문에서는 관계 추출에서 entity의 방향성을 고려하여 relation을 예측하는지 실험하기 위해 문장 수준의 Adversarial Attack과 단어 수준의 Sequence Labeling을 적용하였다. 또한 관계 추출에서 문장에 대한 이해를 높이기 위해 BERT모델을 적용하여 실험을 진행하였다. 실험 결과 관계 추출에서 entity에 대한 방향성을 고려하지 않음을 확인하였다.

• ETRI Journal
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• 제46권1호
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• pp.59-70
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• 2024

We introduce a high-performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta-pseudo-label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human- and pseudo-labeled data. Second, the influence of noisy pseudo-labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human-labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.

• Genomics & Informatics
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• 제19권3호
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• pp.22.1-22.5
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• 2021

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2001년도 International Conference CALS/EC KOREA
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• pp.637-649
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• 2001

ㆍ Research Method - Experimental Design ㆍWhen Entity Bean is deployed and Client request to inquire a specific information of Doctor Table, we experiment Total Time for Query Execution according to Time Measurement Operation in Client code. ㆍWe can apply the statistics for the experiment to the design of Entity Beans.(omitted)

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2002년도 춘계학술발표논문집 (하)
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• pp.1111-1114
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• 2002

최근 정보추출에 대한 중요성이 점차 증가하면서 정보추출에서 필요로 하는 Named Entity와 Coreference, Information Extraction, Information Retrieval의 소개와 한국어에 대해 적용시키기 위한 정의와 방법을 제시한다. 또한, 대량의 문서에 대한 태깅을 효율적으로 수행할 수 있도록 Named Entity와 Coreference 태깅을 쉽게 할 수 있는 NE-CO 태깅 도구를 개발하였다. 이 태깅 도구를 이용하여 시험적으로 경제, 공연, 여행 분야의 300문서에 대한 말뭉치를 구축하였으며, 이 말뭉치는 한국어 정보추출 시스템을 개발하는데 기초 자료로서 활용될 예정이다.