• 정보관리학회지
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• 제22권3호
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• pp.289-306
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• 2005

FRBR 모델에서는 서지 요소와 관계를 중심으로 ER 모델링 방식을 제공하고 있지만, 단지 구조적 프레임워크로서 FRBR 모델을 효율적으로 구현할 수 있는 도구가 필요하다. 본 연구에서는 Topic Maps를 이용하여 FRBR 모델을 구현하는 방법을 제시한다. Topic Maps 기반의 FRBR 모델 구현의 유효성을 실증적으로 보이기 위하여, 명성황후라는 주제와 관련된 MARC 데이터를 추출하여 FRBR 모델을 설계하였고, Topic Maps를 이용하여 이를 구현하였다. 연구 결과, FRBR의 entity-relation과 Topic Maps의 topic-association이 개념적으로 동일하기 때문에 FRBR 모델 개발의 적합함을 알 수 있었다. FRBR 구조는 Topic Maps 패러다임과 그대로 일치하기 때문에 FRBR 모델은 Topic Maps로 구현함이 바람직하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3274-3292
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• 2013

Unsupervised methods for image segmentation are recently drawing attention because most images do not have labels or tags. A topic model is such an unsupervised probabilistic method that captures latent aspects of data, where each latent aspect, or a topic, is associated with one homogeneous region. The results of topic models, however, usually have noises, which decreases the overall segmentation performance. In this paper, to improve the performance of image segmentation using topic models, we propose two topic masks applicable to topic assignments of homogeneous regions obtained from topic models. The topic masks capture the noises among the assigned topic assignments or topic labels, and remove the noises by replacements, just like image masks for pixels. However, as the nature of topic assignments is different from image pixels, the topic masks have properties that are different from the existing image masks for pixels. There are two contributions of this paper. First, the topic masks can be used to reduce the noises of topic assignments obtained from topic models for image segmentation tasks. Second, we test the effectiveness of the topic masks by applying them to segmented images obtained from the Latent Dirichlet Allocation model and the Spatial Latent Dirichlet Allocation model upon the MSRC image dataset. The empirical results show that one of the masks successfully reduces the topic noises.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권9호
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• pp.461-466
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• 2016

토픽 확장은 학습된 토픽의 질을 향상시키기 위해 추가적인 외부 데이터를 반영하여 점진적으로 토픽을 확장하는 방법이다. 기존의 온라인 학습 토픽 모델에서는 외부 데이터를 확장에 사용될 경우, 새로운 단어가 기존의 학습된 모델에 반영되지 않는다는 문제가 있었다. 본 논문에서는 무한 사전 온라인 LDA 토픽 모델을 이용하여 외부 데이터를 반영한 토픽 모델 확장 방법을 연구하였다. 토픽 확장 학습에서는 기존에 형성된 토픽과 추가된 외부 데이터의 단어와 유사도를 반영하여 토픽을 확장한다. 실험에서는 기존의 토픽 확장 모델들과 비교하였다. 비교 결과, 제안한 방법에서 외부 연관 문서 단어를 토픽 모델에 반영하기 때문에 대본 토픽이 다루지 못한 정보들을 토픽에 포함할 수 있었다. 또한, 일관성 평가에서도 비교 모델보다 뛰어난 성능을 나타냈다.

• 한국정보시스템학회지:정보시스템연구
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• 제30권1호
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• pp.45-64
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• 2021

Purpose The traditional topic modeling technique makes it difficult to distinguish the semantic of topics because the key words assigned to each topic would be also assigned to other topics. This problem could become severe when the number of online reviews are small. In this paper, the extended model of topic modeling technique that can be used for analyzing a small amount of online reviews is proposed. Design/methodology/approach The extended model of being proposed in this paper is a form that combines the traditional topic modeling technique and the Word2Vec technique. The extended model only allocates main words to the extracted topics, but also generates discriminatory words between topics. In particular, Word2vec technique is applied in the process of extracting related words semantically for each discriminatory word. In the extended model, main words and discriminatory words with similar words semantically are used in the process of semantic classification and naming of extracted topics, so that the semantic classification and naming of topics can be more clearly performed. For case study, online reviews related with Udo in Tripadvisor web site were analyzed by applying the traditional topic modeling and the proposed extension model. In the process of semantic classification and naming of the extracted topics, the traditional topic modeling technique and the extended model were compared. Findings Since the extended model is a concept that utilizes additional information in the existing topic modeling information, it can be confirmed that it is more effective than the existing topic modeling in semantic division between topics and the process of assigning topic names.

• 정보과학회 논문지
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• 제41권9호
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• pp.652-665
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• 2014

생물학 도메인은 약어 표현이 빈번하며, 실제로 문서에서 중요한 의미를 지니는 개체명들이 약어로 표현되는 경우가 많다. 본 연구에서는 토픽과 링크 정보를 이용하여 약어 중의성을 해결하고 동일한 의미를 가지는 다양한 형태의 약어 원형들(variant forms)에 대한 그룹핑을 시도한다. 이를 위하여 LDA(latent Dirichlet allocation) 기반 의미적 의존 링크 토픽 모델(semantic dependency topic model)을 제안한다. 해당 모델은 생성 모델(generative model)의 일종으로 문서 집합의 각 문서에 등장하는 단어들은 문서에서 발생하는 토픽 분포와 토픽 당 단어 분포에 의해 생성되어 있는 것으로 가정하고, 관측 가능한 문서 집합의 단어들로부터 문서에 내재된 숨어있는 토픽 구조를 추론하여 단어 생성과 토픽 파라미터를 연결시킨다. 본 연구에서는 토픽 정보 외에 단어들 사이에 존재하는 의미적 의존성(semantic dependency)을 링크로 정의하고, 단어 간에 존재하는 링크 정보, 특히 원형과 문장에서 공기하는 단어들 사이의 링크를 파라미터화하여 중의성 해결에 이용하였다. 결과적으로 주어진 문서에 등장하는 약어에 대해 가장 가능성 있는 원형은 해당 모델을 이용하여 추론된 단어-토픽, 문서-토픽, 단어-링크 확률에 의해서 결정된다. 제안하는 모델은 MEDLINE 초록으로부터 Entrez 인터페이스를 이용해 22개의 약어 집합과 186개의 가능한 약어 원형을 이용하여 질의를 생성하고, 이를 이용해 검색된 문서들을 대상으로 학습과 테스트에 이용하였다. 실험은, 주어진 문서에 등장하는 해당 약어에 대한 원형이 무엇인지 예측하는 방식으로 98.3%의 정확률의 높은 성능을 보였다.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.329-342
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• 2020

In recent years, emotional text classification is one of the essential research contents in the field of natural language processing. It has been widely used in the sentiment analysis of commodities like hotels, and other commentary corpus. This paper proposes an improved W-LDA (weighted latent Dirichlet allocation) topic model to improve the shortcomings of traditional LDA topic models. In the process of the topic of word sampling and its word distribution expectation calculation of the Gibbs of the W-LDA topic model. An average weighted value is adopted to avoid topic-related words from being submerged by high-frequency words, to improve the distinction of the topic. It further integrates the highest classification of the algorithm of support vector machine based on the extracted high-quality document-topic distribution and topic-word vectors. Finally, an efficient integration method is constructed for the analysis and extraction of emotional words, topic distribution calculations, and sentiment classification. Through tests on real teaching evaluation data and test set of public comment set, the results show that the method proposed in the paper has distinct advantages compared with other two typical algorithms in terms of subject differentiation, classification precision, and F1-measure.

• 한국정보시스템학회지:정보시스템연구
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• 제30권3호
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• pp.201-216
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• 2021

Purpose The purpose of this study is to analyze the trend of patent technology in textile materials using text mining methodology based on Dynamic Embedded Topic Model and Structural Topic Model. It is expected that this study will have positive impact on revitalizing and developing textile materials industry as finding out technology trends. Design/methodology/approach The data used in this study is 866 domestic patent text data in textile material from 1974 to 2020. In order to analyze technology trends from various aspect, Dynamic Embedded Topic Model and Structural Topic Model mechanism were used. The word embedding technique used in DETM is the GloVe technique. For Stable learning of topic modeling, amortized variational inference was performed based on the Recurrent Neural Network. Findings As a result of this analysis, it was found that 'manufacture' topics had the largest share among the six topics. Keyword trend analysis found the fact that natural and nanotechnology have recently been attracting attention. The metadata analysis results showed that manufacture technologies could have a high probability of patent registration in entire time series, but the analysis results in recent years showed that the trend of elasticity and safety technology is increasing.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권11호
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• pp.585-592
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• 2016

글이나 대화를 일정한 주제의 단위로 나누는 것을 주제 분리라고 한다. 지금까지 주제 분리는 주로 완결된 하나의 문서에서 최적화된 분리를 찾는 방향으로 진행되어 왔다. 하지만 몇몇 응용은 글이나 대화가 진행 중에 주제 분리를 할 필요가 있다. 본 논문에서는 합성곱 신경망을 이용한 교사 학습 모델을 통해 문장의 진행 중에 주제 분리를 수행하는 모델에 대해 제안한다. 그리고 제안한 모델의 성능 검증을 위해 On-line 상황을 가정한 실험과 기존의 C99모델을 결합한 실험을 수행하였다. 실험결과 각각 17.8과 11.95의 Pk 점수를 얻었고, 이를 통해 본 논문의 모델을 통한 On-line 상황에서의 주제 분리 활용의 가능성을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5905-5926
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• 2017

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.