• Journal of Multimedia Information System
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• v.8 no.4
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• pp.221-232
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• 2021

One bottleneck of Bengali to Hindi transfer based machine translation system is the translation of suffixes of noun. The appropriate translation of a nominal suffix often depends on the semantic role of the corresponding noun chunk in the sentence. With the availability of a high performance Bengali morphological analyzer and a basic Bengali parser it is possible to identify the role of each noun chunk. This information may be used for building rules for translating the ambiguous nominal suffixes. As there are some similarities between the uses of Bengali and Hindi nominal suffixes we find that the rules may be identified by linguistically analyzing corpus data. In this paper, we identify rules for the ambiguous four Bengali nominal suffixes from corpus data and evaluate their performances. This set of rules is able to resolve a majority of the nominal suffix ambiguities in Bengali to Hindi transfer based machine translation system. Using the rules, we are able to translate 98.17% Bengali nouns correctly which is much better than the baseline ILMT system's accuracy of 62.8%.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.32-43
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• 2001

Due to the limitation of data modeling power and the view update ambiguity, relational view is limitedly used for engineering applications. On the contrary, object-oriented database view would playa vital role in defining custom interface for engineering applications because the above two limitations of the relational view are overcome by the object-oriented view. Above all, engineering application data interface should fully support updates. More specifically, updates against the data interface needs to be unambiguously defined and its semantic behavior should be equal to base schema updates'. For this purpose, we define the notion of update semantic preserving which means that view updates displays the same semantics as base schema. Besides, in order to show the feasibility of this characteristics, specific and concrete algorithms for update preserving updates are presented for a CAD specialized object-oriented database view - MultiView. This paper finds that in order that virtual classes coudld form a schema with 'isa' relationships rather than just a group of classes, the update semantics on the virtual classes should be defined such that the implied meaning of 'isa' relationships between classes are not to be violated. Besides, as its sufficiency conditions, we derived the update semantics and schema constituable conditions of the virtual classes that make view schemas look like base schemas. To my best knowledge, this is the first research that presents the sufficiency conditions by which we could defined object-oriented views as integrated schemas rather than as separate classes.

• Journal of Gifted/Talented Education
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• v.25 no.5
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• pp.673-696
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• 2015

Students' perception of science-related career strongly influences the formation of career motivation in science. Especially, the high level of science gifted students' positive perceptions plays an important role in allowing them to continue to study science. This study compared perceptions of science-related career between general and gifted middle school students using semantic network analysis. To ensure this end, we first structuralize semantic networks of science-related careers that students perceived. Then, we identified the characters of networks that two different student groups showed based on the structure matrix indices of semantic network analysis. The findings illustrated that the number of science-related careers shown in science gifted students' answer is more than in general students' answer. In addition, the science gifted students perceived more diverse science-related careers than general students. Second, scientific career such as natural scientists and professors were shown in the core of science gifted students' perception network whereas non-research oriented careers such as science teachers and doctors were shown in the core of general students' perception network. In this study, we identified the science gifted students' perceptions of science-related career was significantly different from the general students'. The findings of current study can be used for the science teachers to advise science gifted students on science-related careers.

• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.41-67
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• 2006

The writings frame and various rules based on discourse structure and knowledge-based methods were applied to construct the automatic Ext/sums (extracts & summaries) system from the straight news in web. The frame contains the slot and facet represented by the role of paragraphs, sentences , and clauses in news and the rules determining the type of slot. Rearrangement like Unification, separation, and synthesis of the candidate sentences to summary, maintaining the coherence of meanings, was carried out by using the rules derived from similar degree measurement, syntactic information, discourse structure, and knowledge-based methods and the context plots defined with the syntactic/semantic signature of noun and verb and category of verb suffix. The critic sentence were tried to insert into summary.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.227-232
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• 2020

본 논문은 의미역 주석(Semantic Role Labeling) 자원인 FrameNet을 준구어 말뭉치인 드라마 대본에 주석하는 과정과 주석 결과에 대해 서술한다. 본 논문에서는 프레임 - 프레임 논항 구조의 주석 범위를 한 문장에서 여러 발화로 이루어진 장면 (Scene) 단위의 대본으로 확장하여 문장 내에서 생략된 프레임 논항(Null-Instantiated Frame Elements)을 장면 단위 대본 내의 다른 발화에서 복원하였다. 본 논문은 프레임 자동 분석기를 통해 동일한 드라마의 한국어, 영어 대본에 FrameNet 주석을 한 드라마 대본을 선발된 주석자에 의해 대상 어휘 적합성 평가, 프레임 적합성 평가, 생략된 프레임 논항 복원을 실시하고, 자동 주석된 대본과 주석자 작업 후의 대본 결과를 비교한 결과와 예시를 제시한다. 주석자가 자동 주석된 대본 중 총 2,641개 주석 (한국어 1,200개, 영어 1,461개)에 대하여 대상 어휘 적합성 평가를 실시하여 한국어 190개 (15.83%), 영어 226개 (15.47%)의 부적합 대상 어휘를 삭제하였다. 프레임 적합성 평가에서는 대상 어휘에 자동 주석된 프레임의 적합성을 평가하여 한국어 622개 (61.68%), 영어 473개 (38.22%)의 어휘에 대하여 새로운 프레임을 부여하였다. 생략된 프레임 논항을 복원한 결과 작업된 평균 프레임 논항 개수가 한국어 0.780개에서 2.519개, 영어 1.290개에서 2.253개로 증가하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1220-1228
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• 2009

Ontologies, which are the core of the Semantic Web, are usually limited by specific domains or created by defining meanings and relationships that depend on the heuristic. However, the creation of an ontology is not only very difficult but also very time-consuming. In contrast with ontologies that are used in specific fields, an ontology for the Web entails an unlimited scope of knowledge and expression of information. Hence, it is hard to express information in the same way that is used to create ontologies in specific fields. Therefore, the automatic generation of an ontology takes very important role in the Semantic Web. In this paper, to make ontologies automatically, we suggest the methods to create and renew ontologies by expanding keywords related to the index-terms which are extracted from the search keywords which users input in the search engines by analyzing the morphemes.

• Korean Journal of Cognitive Science
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• v.24 no.4
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• pp.301-321
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• 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.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.911-921
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• 2017

The purpose of this study was to investigate the semantic network analysis to understand image contents and to examine the degree to which words, word clusters contributed to the formation of semantic map within image contents. For this research, from 1993 until 2016 the field of the image contents were collected for a total of 2,624 cases papers. The word appeared in Title analyzed the social network by using the R program of Big Data. The results were as follows: First, Research on 'education' in the field of image contents has decreased. Second, the role of 'media' in the field of image contents is changing. Finally, It is a change in the status of 'contents' in the field of image contents.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.35-44
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• 2011

Ontologies are playing an important role in semantic web which is emerging as a next stage of the web revolution because various kinds of metadata are described in ontologies. Correspondingly, many query languages like SPARQL, RDQL etc. have been proposed for querying these ontologies. But users have to know the structures and resource names of ontologies completely to get search results even if they have expertise on complex formal logic and syntax of the query languages. Especially, casual users do not know the resource names and may use different words from resource names when they write their query language. This vocabulary gap problem have to be solved to raise the success rate. In this paper, an approach for translating user's search words to corresponding resource names has been proposed. This approach uses semantic similarity between user created search words and ontology resource names.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.