• Journal of Information Processing Systems
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• 제16권3호
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Journal of Computing Science and Engineering
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• 제9권2호
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• pp.73-82
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• 2015

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.586-598
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• 2021

Nowadays, users' reviews and feedback on e-commerce sites stored in text create a huge source of information for analyzing customers' experience with goods and services provided by a business. In other words, collecting and analyzing this information is necessary to better understand customer needs. In this study, we first collected a corpus with 99,322 customers' comments and opinions in English. From this corpus we chose the best number of topics (K) using Perplexity and Coherence Score measurements as the input parameters for the model. Finally, we conducted an experiment using the latent Dirichlet allocation (LDA) topic model with K coefficients to explore the topic. The model results found hidden topics and keyword sets with high probability that are interesting to users. The application of empirical results from the model will support decision-making to help businesses improve products and services as well as business management and development in the field of hotel services.

• 마이크로전자및패키징학회지
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• 제27권2호
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• pp.53-58
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• 2020

전기자동차용 전력반도체 패키징 기술에 대한 분석을 수행하였다. 비정형 데이터인 특허들을 수집하여 유효특허를 도출하여 LDA 기법을 적용한 토픽모델링을 수행하였다. 20개의 토픽으로 분류하였고 각 토픽별 추출된 단어를 통해 기술에 대한 정의를 내렸다. 각 토픽의 대한 동향분석을 위해 연도별 빈도수에 대한 회귀분석을 통해 토픽별 Hot토픽과 Cold 토픽을 도출하여 전력반도체 패키징 기술의 동향을 분석하였다. Hot 토픽의 기술로는 내전압에 따른 패키지 구조 기술과 입출력 관련 제어 기술, 방열기술을 도출하였고 Cold 토픽 기술로는 인덕턴스 저감기술이 도출되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2627-2647
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• 2016

With the rapid growth of microblog, expert identification on microblog has been playing a crucial role in many applications. While most previous expert identification studies only assess global authoritativeness of a user, there is no way to differentiate the authoritativeness in a particular aspect of topics. In this paper, we propose a novel model, which jointly models text and following relationship in the same generative process. Furthermore, we integrate a similarity-based weight scheme into the model to address the popular bias problem, and use followee topic distribution as prior information to make user's topic distribution more precisely. Our empirical study on two large real-world datasets shows that our proposed model produces significantly higher quality results than the prior arts.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.130-145
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• 2022

Games are widely used in many fields, but not all games are successful. Then what makes games successful? The question gave us the motivation of this paper, which is to identify critical factors for successful games with topic modeling technique. It is supposed that game reviews written by experts sit on abundant insights and topics of how games succeed. To excavate these insights and topics, latent Dirichlet allocation, a topic modeling analysis technique, was used. This statistical approach provided words that implicate topics behind them. Fifty topics were inferred based on these words, and these topics were categorized by stimulation-response-desiregoal (SRDG) model, which makes a streamlined flow of how players engage in video games. This approach can provide game designers with critical factors for successful games. Furthermore, from this research result, we are going to develop a model for immersive game experiences to explain why some games are more addictive than others and how successful gamification works.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3168-3186
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• 2020

Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.

• 스마트미디어저널
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• 제10권1호
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• pp.39-46
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• 2021

본 연구는 뉴스 기사에서 기사문을 구성하는 문장별 중요도를 고려하여 요약문을 추출하는 방법에 관한 것으로 문장 중요도에 영향을 주는 특성으로 중심 문장(Topic Sentence)일 확률, 기사 제목 및 다른 문장과의 유사도, 문장 위치에 따른 가중치를 추출하여 문장 중요도를 계산하는 방법을 제안한다. 이때, 중심 문장(Topic Sentence)은 일반 문장과는 구별되는 특징을 가질 것이라는 가설을 세우고, 딥러닝 기반 분류 모델을 학습시켜 입력 문장에 대한 중심 문장 확률값을 구한다. 또한 사전학습된 ELMo 언어 모델을 활용하여 문맥 정보를 반영한 문장 벡터값을 기준으로 문장간 유사도를 계산하여 문장 특성으로 추출한다. LSTM 및 BERT 모델의 중심 문장 분류성능은 정확도 93%, 재현율 96.22%, 정밀도 89.5%로 높은 분석 결과가 나왔으며, 이렇게 추출된 문장 특성을 결합하여 문장별 중요도를 계산한 결과, 기존 TextRank 알고리즘과 비교하여 중심 문장 추출 성능이 10% 정도 개선된 것을 확인할 수 있었다.

• 정보관리연구
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• 제36권4호
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• pp.155-174
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• 2005

최근 국가적 필요성의 인식 하에 과학기술정보, 특히 연구개발정보의 종합적 수집 관리 및 유통을 위한 국가 과학기술 종합정보 시스템(NTIS)의 개발이 논의되고 있다. 국가 과학기술 종합정보 시스템은 연구개발 프로세스, 연구개발활동, 연구개발정보, 연구개발 데이터 레벨의 구성요소들 간의 유기적 연계를 그 근간으로 한다. 본 논문에서는 국가 과학기술 종합정보 시스템의 개발에 기반이 되는 연구개발정보들 간의 상호연계 모델을 제안한다. 연계를 위해 고려되는 연구개발정보는 연구과제정보, 연구인력정보, 연구성과정보로 연구개발활동에 있어 매우 중요한 정보라 할 수 있다. 제안된 연계 모델을 통해 연구개발정보들 간의 상호 참조 및 탐색이 가능하며, 다양한 관점에서 다양한 형태의 질의를 수행할 수 있다. 연계 모델은 ISO13250 표준인 XML Topic Map을 이용하여 개발되었으며, 예제를 통해 연계 모델의 우수성 및 향후 활용가능성을 살펴보았다.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1096-1107
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• 2019

In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.