• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.199-206
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• 2021

Rhetorical relations between two text fragments are essential information and support natural language processing applications such as Question - Answering (QA) system and automatic text summarization to produce an effective outcome. Question - Answering (QA) system facilitates users to retrieve a meaningful response. There is a demand for rhetorical relation based datasets to develop such a system to interpret and respond to user requests. There are a limited number of datasets for developing an Arabic QA system. Thus, there is a lack of an effective QA system in the Arabic language. Recent research works reveal that unsupervised learning can support the QA system to reply to users queries. In this study, researchers intend to develop a rhetorical relation based dataset for implementing unsupervised learning applications. A web crawler is developed to crawl Arabic content from the web. A discourse-annotated corpus is generated using the rhetorical structural theory. A Naïve Bayes based QA system is developed to evaluate the performance of datasets. The outcome shows that the performance of the QA system is improved with proposed dataset and able to answer user queries with an appropriate response. In addition, the results on fine-grained and coarse-grained relations reveal that the dataset is highly reliable.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.81-83
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• 2022

Understanding research trends is necessary to select research topics and explore related works. Most researchers search representative keywords of interesting domains or technologies to understand research trends. However some conferences in artificial intelligence or data mining fields recently publish hundreds to thousands of papers for each year. It makes difficult for researchers to understand research trend of interesting domains. In our paper, we propose an automatic technology keyphrase extraction method to support researcher to understand research trend for each conference or journal. Keyphrase extraction that extracts important terms or phrases from a text, is a fundamental technology for a natural language processing such as summarization or searching, etc. Previous keyphrase extraction technologies based on pretrained language model extract keyphrases from long texts so performances are degraded in short texts like titles of papers. In this paper, we propose a techonolgy keyphrase extraction model that is robust in short text and considers the importance of the word.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• ETRI Journal
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• v.24 no.1
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• pp.1-11
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• 2002

A video summary abstracts the gist from an entire video and also enables efficient access to the desired content. In this paper, we propose a novel method for summarizing news video based on multimodal analysis of the content. The proposed method exploits the closed caption data to locate semantically meaningful highlights in a news video and speech signals in an audio stream to align the closed caption data with the video in a time-line. Then, the detected highlights are described using MPEG-7 Summarization Description Scheme, which allows efficient browsing of the content through such functionalities as multi-level abstracts and navigation guidance. Multimodal search and retrieval are also within the proposed framework. By indexing synchronized closed caption data, the video clips are searchable by inputting a text query. Intensive experiments with prototypical systems are presented to demonstrate the validity and reliability of the proposed method in real applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.473-476
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• 2006

인터넷의 발달로 인하여 정보의 양은 시간이 지날수록 폭발적으로 증가하고 있다. 이러한 방대한 정보로부터 정보검색시스템은 사용자에게 너무 많은 검색결과를 제시하여 사용자가 원하는 정보를 찾기 위해 너무 많은 시간을 소요하게 하는 정보의 과적재 문제가 있다. 질의 기반의 문서요약은 정보의 사용자가 원하는 정보의 검색시간을 줄임으로써 정보의 과적재 문제를 해결하는 방법으로서 점차 중요성이 증가하고 있다. 본 논문은 비음수 행렬 인수분해 (NMF, Non-negative Matrix Factorization)과 코사인 유사도를 이용하여 질의 기반의 문서를 요약하는 새로운 방법을 제안하였다. 제안된 방법은 질의와 문서 간에 사전학습이 필요 없다. 또한 문서를 그래프로 변형시키는 복잡한 처리 없이 NMF 에 의해 얻어진 의미 특징(semantic feature)과 의미 변수(semantic variable)로 문서의 고유 구조를 반영하여 요약의 정확도를 높일 수 있다. 마지막으로 단순한 방법으로 문장을 쉽게 요약할 수 있다.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.323-336
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• 2006

In this paper, we present a pilot system that can extract paraphrases from a parallel corpus using to-training method. Paraphrases are useful for the applications that should rreate a varied ind fluent text, such as machine translation, question-answering system, and multidocument summarization system. One of the difficulties in extracting paraphrases is to find a rich source from which we can extract paraphrases. The bible is one of the good sources fur extracting paraphrases as it has several Korean versions in which every sentence can be easily aligned by the chapter and the verse. We ran extract not only the lexical-level paraphrases but also the phrasal-level paraphrases from the parallel corpus which consists of the bibles using co-training method.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.174-181
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• 2007

정보 검색의 결과로 나타나는 요약문을 스니펫(snippet)이라 한다. 사용자는 자신이 원하는 정보를 얻기 위해 문서를 검색하는데, 이 때 스니펫은 사용자가 원하는 문서를 찾는데 중요한 역할을 한다. 본 논문에서는 정보검색 분야에서 높은 성능을 보이는 유사 적합성 피드백을 자동 문서 요약에 맞게 적용하여 높은 성능의 스니펫 생성 시스템을 구현한다. 우선, 사용자의 질의가 포함된 문장들을 일차적으로 요약 문장 후보로 추출한다. 그리고 추출된 문장 후보로부터 명사들을 질의 후보로 고려한다. 각 문장이 질의의 포함 여부에 따라 문장의 적합성을 판단하게 되고, 유사 적합성 피드백 확률 모델에 적용한 후 질의 후보들의 가중치를 추정하여 가중치 순위를 통해 확장할 질의들을 결정한다. 확장된 질의들과 기존의 질의들의 가중치를 합산하여 각 문장의 순위를 매기게 되고 가장 높은 순위의 문장들이 스니펫으로 제시된다. 논문에서 제안한 기법은 추가적인 핵심 질의들을 자동으로 확장하여 중요한 문장을 추출할 수 있다. 이 연구를 위해서 일반 상용 정보 검색 서비스에서 제공하는 스니펫을 수집하였고 이들의 정확도와 시스템의 정확도를 비교하였다. 실험 결과를 통해 살펴본 제안된 시스템의 성능은 상용 정보 검색기에서 제공되고 잇는 스니펫의 정확도 보다 우수한 성능을 보였다.

• Journal of The Korean Association For Science Education
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• v.15 no.4
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• pp.394-403
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• 1995

The purpose of this study was to find out and analyze the science teacher's teaching methods. A total of 35 teaching methods were abstracted from the previous studies and the relating literatures. An instrument to measure the frequencies of using methods was developed and then tested to middle school science teachers. The Results of two factor analysis methods were compared. The results are as follows: The instruments's reliablity coefficient(Cronbach ${\alpha}$) was 0.7707. The teaching methods which middle school science teachers have used frequently were represented as the proposing of the learning objectives, the deductive teaching, the experimental activities by teacher's guide, the summarization after explanation, the reading text etc. Also, it was revealed that they have not use the diagnostic evaluation, the formative evaluation, the experimental activities by student's design, the instructional medium. By confirmatory factor analysis, the 1st factor included 13 teaching methods and 2nd and 3rd factor included 9 and 7 methods respectedly. The meaning of 1st factor was interpreted to stimulate student's learning motives. And the other's were about the development of instruction. In exploratory factor analysis factors were overlapped or more fined. These were due to the structure of factors.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1254-1271
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• 2018

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.611-614
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• 2002

문서 자동 요약은 입력된 문서에 대해 컴퓨터가 자동으로 요약을 생성하는 과정을 의미한다. 즉, 컴퓨터가 문서의 기본적인 내용을 유지하면서 문서의 복잡도 즉 문서의 길이를 줄이는 작업이다. 효율적인 정보 접근을 제공함과 동시에 정보 과적재를 해결하기 위한 하나의 방법으로 문서 자동요약에 관한 연구가 활발히 진행되고 있다. 본 논문의 목적은 어휘 연관성 정보를 이용하여 한국어 문서를 자동으로 요약하는 효율적이며 효과적인 모형을 개발하는 것이다. 제안한 방법에서는 신문기사와 같은 특정 부류에 국한되는 단어간의 어휘연관성을 이용하여 명사-명사 공기패턴과 명사-동사 공기패턴을 구축하여 문서요약에 이용한다. 크게 불용어 처리 단계, 공기패턴 구축 단계, 문장 중요도 계산 단계, 요약 생성단계의 네 단계로 나누어 요약을 생성한다. 30% 중요문장 추출된 신문기사를 대상으로 평가한 결과 명사-명사 공기패턴과 빈도만을 이용한 방법보다 명사-동사 공기패턴을 이용한 방법이 좋은 결과를 가져 왔다.