• Korean Journal of Cognitive Science
• /
• 제24권4호
• /
• pp.301-321
• /
• 2013

For the task of sentence ordering, this paper attempts to utilize the Entity-Grid model, a type of entity-based modeling approach, as well as Latent Semantic analysis, which is based on vector space modeling, The task is well known as one of the fundamental tools used to measure text coherence and to enhance text generation processes. For the implementation of the Entity-Grid model, we attempt to use the syntactic roles of the nouns in the Korean text for the ordering task, and measure its impact on the result, since its contribution has been discussed in previous research. Contrary to the case of German, it shows a positive result. In order to obtain the information on the syntactic roles, we use a strategy of using Korean case-markers for the nouns. As a result, it is revealed that the cues can be helpful to measure text coherence. In addition, we compare the results with the ones of the LSA-based model, discussing the advantages and disadvantages of the models, and options for future studies.

• Language and Information
• /
• 제8권1호
• /
• pp.77-100
• /
• 2004

This paper aims to account for direct combination of an entity type noun with the verb HA- 'do' (ex. piano-rul ha- 'piano-ACC do') in Korean, based on Generative Lexicon Theory (Pustejovsky, 1995). The verb HA-'do' coerces some entity type nouns (e.g., pap 'boiled rice') into event type ones, by virtue of the qualia of the nouns. Typically, a telic-based type coercion supplies individual predication to the HA- construction and an agentive-based type coercion evokes a stage-level interpretation. Type coercion has certain constraints on the choice of qualia. We further point out that qualia cannot be a warehouse of pragmatic information. Qualia are composed of necessary information to explain the lattice structure of lexical meaning and co-occurrence constraints, distinct from accidental information. Finally, we seriously consider co-composition as an alternative to type coercion for the crucial operation of type shift.

• Journal of KIBIM
• /
• 제5권2호
• /
• pp.46-54
• /
• 2015

Vision-based tracking has been proposed as a means to efficiently track a large number of construction resources operating in a congested site. In order to obtain 3D coordinates of an object, it is necessary to employ stereo-vision theories. Detecting and tracking of multiple objects require an entity matching process that finds corresponding pairs of detected entities across the two camera views. This paper proposes an efficient way of entity matching for tracking of construction workers. The proposed method basically uses epipolar geometry which represents the relationship between the two fixed cameras. Each pixel coordinate in a camera view is projected onto the other camera view as an epipolar line. The proposed method finds the matching pair of a worker entity by comparing the proximity of the all detected entities in the other view to the epipolar line. Experimental results demonstrate its suitability for automated entity matching for 3D vision-based tracking of construction workers.

• Journal of KIISE:Software and Applications
• /
• 제35권7호
• /
• pp.405-416
• /
• 2008

To overcome the limitation of keyword-based integrated search, this study shows entity-centric integrated search method using URI scheme. Our system generates entity pages in ways of analyzing user's keyword and instances matched with it, selecting optimal entity type, and calling unit services simultaneously. Topic information extracted from articles is propagated to persons, institutions, and locations by reasoning for providing topic-centric information. With comparative experiments based on search results and usability tests, we proved that this approach is superior to keyword-based integrated search served by CiteSeer and Google Scholar.

• MALSORI
• /
• 제58호
• /
• pp.45-66
• /
• 2006

In this paper, a rule-based (transformation-based) NE recognition system is proposed. This system uses Brill's rule inference approach. The performance of the rule-based system and IdentiFinder, one of most successful stochastic systems, are compared. In the baseline case (no punctuation and no capitalisation), both systems show almost equal performance. They also have similar performance in the case of additional information such as punctuation, capitalisation and name lists. The performances of both systems degrade linearly with the number of speech recognition errors, and their rates of degradation are almost equal. These results show that automatic rule inference is a viable alternative to the HMM-based approach to NE recognition, but it retains the advantages of a rule-based approach.

• The Transactions of the Korea Information Processing Society
• /
• 제6권11S호
• /
• pp.3356-3366
• /
• 1999

Electric commerce system based on distributed mobile on object such as mobile agents need both precise identification and secure authentication scheme on remotemobile entities. In this paper, existing discrete logarithm based Schnorr like entity authentication schemes are improved by the analysis of performance and security on the bi-directional interactive proofs. And ElGamal like schemes are also proposed. Then, there are enhanced with oblivious transfer based mono-directional authentication schemes based on trusted third party for applying to the mobile agent based computing systems. Therefore, proposed schemes provide compatible performance and safety on mobile entity authentication process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
• /
• pp.572-575
• /
• 2008

In this paper we designed a Payment Service. This service is based on Parlay X Web Services. In the design process we described a functional entity and the information flow between entities. Also, we described a physical entity and the information flow between entities.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제15권2호
• /
• pp.111-120
• /
• 2015

Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권6호
• /
• pp.2012-2030
• /
• 2021

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
• /
• pp.218-221
• /
• 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.