• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.9-15
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• 2012

A conventional event sentence extraction research doesn't learn the 3W features in the learning step and applies the rule on whether the 3W feature exists in the extraction step. This paper presents a sentence weight based event sentence extraction method that calculates the weight of the 3W features in the learning step and applies the weight of the 3W features in the extraction step. In the experimental result, we show that top 30% features by the $TF{\times}IDF$ weighting method is good in the feature filtering. In the real estate domain of the public issue, the performance of sentence weight based event sentence extraction method is improved by who and when of 3W features. Moreover, In the real estate domain of the public issue, the sentence weight based event sentence extraction method is better than the other machine learning based extraction method.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.248-265
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• 2015

In this paper, we propose a maximum entropy-based model, which can mathematically explain the bio-molecular event extraction problem. The proposed model generates an event table, which can represent the relationship between an event trigger and its arguments. The complex sentences with distinctive event structures can be also represented by the event table. Previous approaches intuitively designed a pipeline system, which sequentially performs trigger detection and arguments recognition, and thus, did not clearly explain the relationship between identified triggers and arguments. On the other hand, the proposed model generates an event table that can represent triggers, their arguments, and their relationships. The desired events can be easily extracted from the event table. Experimental results show that the proposed model can cover 91.36% of events in the training dataset and that it can achieve a 50.44% recall in the test dataset by using the event table.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.77-97
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• 2017

A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.143-152
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• 2008

Nowadays, many investigators are studying various methodologies concerning event expression for semantic retrieval of video data. However, most of the parts are still using annotation based retrieval that is defined into annotation of each data and content based retrieval using low-level features. So, we propose a method of creation of the motion unit and extracting event through the unit for the more semantic retrieval than existing methods. First, we classify motions by event unit. Second, we define semantic unit about classified motion of object. For using these to event extraction, we create rules that are able to match the low-level features, from which we are able to retrieve semantic event as a unit of video shot. For the evaluation of availability, we execute an experiment of extraction of semantic event in video image and get approximately 80% precision rate.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.138-146
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• 2009

Recently big-sized hospitals showed their interest in medical information event monitoring system based on Sense and Respond in order to give patients in emergency a safer medical treatment. In this paper we implemented medical information event monitoring system based on Sense and Respond which stand on event and monitoring process. This system consists of event definition, event extraction, event delivery, and monitoring. It ensures real-time response by grasping and delivering medical information in real-time, and well-balanced understanding of information for hospital management by real-time tracking. Also early confrontation case by case by, event delivery make the safety of patients be secured, the efficiency of hospital can be maximized by early grasping the problems in hospital management problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3498-3512
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• 2021

This article describes how events that make up text stories can be represented and extracted. We also address the results from our simple experiment on extracting and clustering events in terms of emotions, under the assumption that different emotional events can be associated with the classified clusters. Each emotion cluster is based on Plutchik's eight basic emotion model, and the attributes of the NLTK-VADER are used for the classification criterion. While comparisons of the results with human raters show less accuracy for certain emotion types, emotion types such as joy and sadness show relatively high accuracy. The evaluation results with NRC Word Emotion Association Lexicon (aka EmoLex) show high accuracy values (more than 90% accuracy in anger, disgust, fear, and surprise), though precision and recall values are relatively low.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.125-130
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• 2017

Since field control equipment such as PLC has no function to log key event information in the log, it is difficult to analyze the accident. Therefore, it is necessary to secure information that can analyze when a cyber accident occurs by logging the main event information of the field control equipment such as PLC and IED. The protocol analyzer is required to analyze the field control device (the embedded device) communication protocol for event logging. However, the conventional analyzer, such as Wireshark is difficult to process the data identification and extraction of the large variety of protocols for event logging is difficult analysis of the payload data based and classification. In this paper, we developed a system for Big Data based on field control device communication protocol payload data extraction for event logging of large studies.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.119-123
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• 2020

이벤트 추출은 텍스트에서 구조화된 이벤트를 분석하는 것이다. 본 논문은 대화문에서 발생하는 다양한 종류의 이벤트를 다루기 위해 이벤트 스키마를 프레임넷으로 정한다. 대화문에서의 이벤트 논항은 이벤트가 발생하는 문장 뿐만 아니라 다른 문장 또는 대화에 참여하는 발화자에서 발생할 수 있다. 대화문 주석 데이터의 부재로 대화문에서의 프레임 파싱 연구는 진행되지 않았다. 본 논문이 제안하는 모델은 대화문에서의 이벤트 논항 구간이 주어졌을 때, 논항 구간의 역할을 식별하는 모델이다. 해당 모델은 이벤트를 유발한 어휘, 논항 구간, 논항 역할 간의 관계를 학습한다. 대화문 주석 데이터의 부족을 극복하기 위해 문어체 주석 데이터인 한국어 프레임넷을 활용하여 전이학습을 진행한다. 이를 통해 정확도 51.21%를 달성한다.

• The Journal of Korean Association of Computer Education
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• v.17 no.2
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• pp.31-38
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• 2014

This paper reveals continuity of related events which are occurred and changing from moment to moment accident/events collected from various social media channels. Among them, we especially define the events which have big social influence as "issue event" and investigate the type and characteristics of continuous issue event for each domain. We also introduce a automatic issue detection system in social media text. Based on the extracted issue event results in a particular domain, we analyse the continuity of those events by illustrating in time and place-axis. Furthermore, we identify the relationship between social media in terms of issue events propagation.