• Title/Summary/Keyword: Event Extraction

Search Result 104, Processing Time 0.031 seconds

Crowdsourcing based Local Traffic Event Detection Scheme (크라우드 소싱 기반의 지역 교통 이벤트 검출 기법)

  • Kim, Yuna;Choi, Dojin;Lim, Jongtae;Kim, Sanghyeuk;Kim, Jonghun;Bok, Kyoungsoo;Yoo, Jaesoo
    • The Journal of the Korea Contents Association
    • /
    • v.22 no.4
    • /
    • pp.83-93
    • /
    • 2022
  • Research is underway to solve the traffic problem by using crowdsourcing, where drivers use their mobile devices to provide traffic information. If it is used for traffic event detection through crowdsourcing, the task of collecting related data is reduced, which lowers time cost and increases accuracy. In this paper, we propose a scheme to collect traffic-related data using crowdsourcing and to detect events affecting traffic through this. The proposed scheme uses machine learning algorithms for processing large amounts of data to determine the event type of the collected data. In addition, to find out the location where the event occurs, a keyword indicating the location is extracted from the collected data, and the administrative area of the keyword is returned. In this way, it is possible to resolve a location that is broadly defined in the existing location information or incorrect location information. Various performance evaluations are performed to prove the superiority and feasibility of the proposed scheme.

Study and Experimentation on Detection of Nicks inside of Porcelain with Acoustic Emission

  • Jin, Wei;Li, Fen
    • Journal of Korea Multimedia Society
    • /
    • v.9 no.12
    • /
    • pp.1572-1579
    • /
    • 2006
  • An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

  • PDF

RESEARCH ON SENTIMENT ANALYSIS METHOD BASED ON WEIBO COMMENTS

  • Li, Zhong-Shi;He, Lin;Guo, Wei-Jie;Jin, Zhe-Zhi
    • East Asian mathematical journal
    • /
    • v.37 no.5
    • /
    • pp.599-612
    • /
    • 2021
  • In China, Weibo is one of the social platforms with more users. It has the characteristics of fast information transmission and wide coverage. People can comment on a certain event on Weibo to express their emotions and attitudes. Judging the emotional tendency of users' comments is not only beneficial to the monitoring of the management department, but also has very high application value for rumor suppression, public opinion guidance, and marketing. This paper proposes a two-input Adaboost model based on TextCNN and BiLSTM. Use the TextCNN model that can perform local feature extraction and the BiLSTM model that can perform global feature extraction to process comment data in parallel. Finally, the classification results of the two models are fused through the improved Adaboost algorithm to improve the accuracy of text classification.

Automatic Event Extraction from the Yeast Literature by Pattern Matching (패턴 정보를 이용한 효모 관련 문서에서의 이벤트 자동 추출)

  • 전홍우;황영숙;임해창
    • Proceedings of the Korean Information Science Society Conference
    • /
    • 2003.04c
    • /
    • pp.464-466
    • /
    • 2003
  • 생명과학 관련 문서에서 자동으로 이벤트를 추출하는 것은 관련 연구자들의 연구에 많은 도움을 줄 수 있다. 본 논문에서는 생명과학 관련 문서 중 특히 효모와 관련된 문서를 대상으로 간단한 자연언어 처리 기술을 적용하여 유의미한 정보를 추출한 결과를 제시하고자 한다. 실험은 효모 관련 문서에서 고빈도의 이벤트 표현 동사에 대한 패턴 정보를 조사한 후, 패턴 정보에 의거하여 이벤트를 추출하였다. 평가 결과, 비교적 간단한 자연언어 처리 기술 안으로도 유의미한 정보들을 추출할 수 있었다.

  • PDF

Trend Analysis using Spatial-Temporal Visualization of Event Information based on Social Media (소셜 미디어에 기반한 이벤트 정보의 시공간적 시각화를 통한 추이 분석)

  • Oh, Hyo-Jung;Yun, Bo-Hyun;Yoo, Cheol-Jung;Kim, Yong
    • Journal of Internet Computing and Services
    • /
    • v.15 no.6
    • /
    • pp.65-75
    • /
    • 2014
  • The main focus of this paper is to analyze trend of event informations in a variety of mass media by graphical visualization in axis of the time and location. Especially, continuity analysis based on user-generated social media can reflect the social impact of a certain event according to change time and location and their directional changes. To reveal the characteristics of continuous events, we survey the data set collected from news articles and tweets during two years. Based on case studies on 'disease' and 'leisure', we verify the effectiveness and usefulness of our proposed method. Even though some events occurred during same period, we showed directional changes which have high-impact in social media referred user interest's, compared with fact-based continuous visualization results.

Judgment about the Usefulness of Automatically Extracted Temporal Information from News Articles for Event Detection and Tracking (사건 탐지 및 추적을 위해 신문기사에서 자동 추출된 시간정보의 유용성 판단)

  • Kim Pyung;Myaeng Sung-Hyon
    • Journal of KIISE:Software and Applications
    • /
    • v.33 no.6
    • /
    • pp.564-573
    • /
    • 2006
  • Temporal information plays an important role in natural language processing (NLP) applications such as information extraction, discourse analysis, automatic summarization, and question-answering. In the topic detection and tracking (TDT) area, the temporal information often used is the publication date of a message, which is readily available but limited in its usefulness. We developed a relatively simple NLP method of extracting temporal information from Korean news articles, with the goal of improving performance of TDT tasks. To extract temporal information, we make use of finite state automata and a lexicon containing time-revealing vocabulary. Extracted information is converted into a canonicalized representation of a time point or a time duration. We first evaluated the extraction and canonicalization methods for their accuracy and investigated on the extent to which temporal information extracted as such can help TDT tasks. The experimental results show that time information extracted from text indeed helps improve both precision and recall significantly.

A Method for Detecting Event-Location based on Similar Keyword Extraction in Tweet Text (트윗 텍스트의 유사 키워드 추출을 통한 이벤트 지역 탐지 기법)

  • Yim, Junyeob;Ha, Hyunsoo;Hwang, Byung-Yeon
    • Spatial Information Research
    • /
    • v.23 no.5
    • /
    • pp.1-7
    • /
    • 2015
  • Twitter has the fast propagation and diffusion of information compare to other SNS. Therefore, many researches about detecting real-time event using twitter are progressing. Twitter real-time event detecting system assumes every twitter user as a sensor and analyzes their written tweet in order to detect the event. Researches that are related to this twitter have already obtained good results but confronted the limits because of some problems. Especially, many existing researches are using the method that can trace an event location by using GPS coordinate. However, it can be suggested a definite limitation through the present user's skeptical responses about making personal location information public. Therefore, this paper suggests the method that traces the location information in tweet contents text without using the provided location information from twitter. Associated words were grouped by using the keyword that extracted in tweet contents text. The place that the events have occurred and whether the events have surely occurred are detected by this experiment using this algorithm. Furthermore, this experiment demonstrated the necessity of the suggested methods by showing faster detection compare to the other existing media.

Construction of Event Networks from Large News Data Using Text Mining Techniques (텍스트 마이닝 기법을 적용한 뉴스 데이터에서의 사건 네트워크 구축)

  • Lee, Minchul;Kim, Hea-Jin
    • Journal of Intelligence and Information Systems
    • /
    • v.24 no.1
    • /
    • pp.183-203
    • /
    • 2018
  • News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

Malicious Application Determination Using the System Call Event (시스템 콜 이벤트 분석을 활용한 악성 애플리케이션 판별)

  • Yun, SeokMin;Ham, YouJeong;Han, GeunShik;Lee, HyungWoo
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.4 no.4
    • /
    • pp.169-176
    • /
    • 2015
  • Recently smartphone market is rapidly growing and application market has also grown significantly. Mobile applications have been provided in various forms, such as education, game, SNS, weather and news. And It is distributed through a variety of distribution channels. Malicious applications deployed with malicious objectives are growing as well as applications that can be useful in everyday life well. In this study, Events from a malicious application that is provided by the normal application deployment and Android MalGenome Project through the open market were extracted and analyzed. And using the results, We create a model to determine whether the application is malicious. Finally, model was evaluated using a variety of statistical method.

Event Template Extraction for the Decision Support based on Social Media (소셜미디어 기반 의사결정 지원을 위한 이벤트 템플릿 추출)

  • Heo, Jeong;Ryu, Pum-Mo;Choi, Yoon-Jae;Kim, Hyun-Ki
    • Annual Conference on Human and Language Technology
    • /
    • 2012.10a
    • /
    • pp.53-57
    • /
    • 2012
  • 본 논문은 소셜 미디어 기반 의사결정 지원 시스템인 '소셜위즈덤'에 포함된 이벤트 템플릿 추출에 대해서 소개한다. 의사결정 지원 시스템은 경제적, 사회적 중요사항을 결정할 수 있도록 관련 정보와 인사이트(Insight)를 제공하는 정보시스템을 이른다. 기존 시스템은 단지 특정 키워드 빈도나 공기하는 키워드들의 관계만을 제공하였다. 그러나, 소셜위즈덤은 이벤트로 정의되는 주체(Subject), 이벤트 속성(Event-Property), 객체(Object)의 트리플(Triple) 집합인 템플릿을 추출하여 이를 기반으로 이벤트 정보를 함께 제공한다. 템플릿 추출은 고정밀 언어분석의 관계추출 기술과 온톨로지에 기반한 템플릿 제약 및 필터링 규칙을 이용하였다. 수작업으로 구축한 평가데이터로 평가한 결과, 템플릿 추출 성능(F-Score)은 뉴스 0.544, 블로그 0.3386, 트위터 0.3251이고 전체 통합 성능은 0.4648이었다. 필터링 성능(Accuracy)은 뉴스 0.7257, 블로그 0.6122, 트위터 0.6207이고 전체 통합 성능은 0.722이었다.

  • PDF