• 한국언어정보학회지:언어와정보
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• 제14권2호
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• pp.47-70
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• 2010

This study investigates the semantic and prosodic properties of the so-called contrastive topic. We posit two informational primitives, namely, topical feature [+-T] and focal feature [+-F], from which four different informational categories, i.e., [+T, +F], [+T, -F], [-T, +F], and [-T, -F], are yielded. It is proposed that the informational category of contrastive topic has focal property [+F] as well as topical property [+T]. Based on the semantic approach that regards the function of [+F] as identificational predication and that of [+T] as forming a semantic conditional clause, it is shown that the semantic function of contrastive topic, which is specified as [+T, +F], is the combination of these two functions, i.e., identificational predication in a semantic conditional clause. This is supported by a scrutinized exploration of the prosodic pattern of English contrastive topic.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권12호
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• pp.774-779
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• 2008

본 논문에서는 토픽 시그너처(Topic Signature)를 이용하여 댓글을 분류하는 시스템에 대해서 설명한다. 토픽 시그너처는 자질을 선택하는 방법으로 문서요약이나 문서분류에서 사용하는 방법이다. 댓글은 문장의 길이가 짧고 띄어쓰기가 거의 없으며 특수문자들이 많은 특성을 가지고 있다. 따라서 우리는 댓글을 7개의 음절로 나누고 이를 다시 Tri-gram으로 나누어 분류의 기본단위로 본다. 이 Tri-gram을 토픽 시그너처를 이용한 학습 단위로 사용하고, 학습한 자질을 베이지안(Bayesian) 모델을 사용하여 분류한다. 다양한 방법의 모델과 비교 실험을 통하여 구현한 시스템의 성능이 기존의 방법보다 상승되었음을 실험 결과를 통해 알 수 있었다.

• 정보관리학회지
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• 제35권2호
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• pp.63-88
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• 2018

본 연구는 LDA 토픽 모델과 딥 러닝을 적용한 단어 임베딩 기반의 Doc2Vec 기법을 활용하여 자질을 선정하고 자질집합의 크기와 종류 및 분류 알고리즘에 따른 분류 성능의 차이를 평가하였다. 또한 자질집합의 적절한 크기를 확인하고 문헌의 위치에 따라 종류를 다르게 구성하여 분류에 이용할 때 높은 성능을 나타내는 자질집합이 무엇인지 확인하였다. 마지막으로 딥 러닝을 활용한 실험에서는 학습 횟수와 문맥 추론 정보의 유무에 따른 분류 성능을 비교하였다. 실험문헌집단은 PMC에서 제공하는 생의학 학술문헌을 수집하고 질병 범주 체계에 따라 구분하여 Disease-35083을 구축하였다. 연구를 통하여 가장 높은 성능을 나타낸 자질집합의 종류와 크기를 확인하고 학습 시간에 효율성을 나타냄으로써 자질로의 확장 가능성을 가지는 자질집합을 제시하였다. 또한 딥 러닝과 기존 방법 간의 차이점을 비교하고 분류 환경에 따라 적합한 방법을 제안하였다.

• 응용통계연구
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• 제35권6호
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• pp.739-754
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• 2022

텍스트 데이터는 일반적으로 많은 변수를 포함하고 있으며 변수들 사이의 연관성도 높아 통계 분석의 정확성, 효율성 등에서 문제가 생길 수 있다. 이러한 문제점에 대처하기 위해 목표 변수가 주어진 지도 학습에서는 목표 변수를 잘 설명할 수 있는 단어들을 선택하여 이 단어들만 통계 분석에 이용하기도 한다. 반면, 비지도 학습에서는 목표 변수가 주어지지 않으므로 지도 학습에서와 같은 단어 선택 절차를 활용하기 어렵다. 이 연구에서는 토픽 모형을 이용하여 지도 학습에서의 목표 변수를 대신할 수 있는 토픽을 생성하고 각 토픽별로 연관성이 높은 단어들을 선택하는 단어 선택 절차를 제안한다. 제안된 절차를 실제 텍스트 데이터에 적용한 결과, 단어 선택 절차를 이용하면 많은 토픽에서 공통적으로 자주 등장하는 단어들을 제거함으로써 토픽을 더 명확하게 식별할 수 있었다. 또한, 군집 분석에 적용한 결과, 군집과 범주 사이에 높은 연관성을 가지는 군집 분석 결과를 얻을 수 있는 것으로 나타났다. 목표 변수에 대한 정보없이 토픽 모형을 이용하여 선택한 단어들을 분류 분석에 적용하였을 때 목표 변수를 이용하여 단어들을 선택한 경우와 비슷한 분류 정확성을 얻을 수 있음도 확인하였다.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.55-66
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• 2023

Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including "reader community-opinion leader-text content" was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-of-the-art approaches, which indicates that the UIT has better performance in the smooth time dimension.

• Journal of Computing Science and Engineering
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• 제10권4호
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• pp.128-136
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• 2016

It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.

• 한국IT서비스학회지
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• 제17권2호
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• pp.191-205
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• 2018

With the advancement of artificial intelligence (AI) techniques, many consumer products have adopted AI features for providing proactive and personalized services to customers. One of the most prominent products featuring AI techniques is a smart speaker. The fundamental of smart speaker is a portable wireless Internet connecting speaker which already have existed in a consumer market. By applying AI techniques, smart speakers can recognize human voices and communicate with them. In addition, they can control other connecting devices and provide offline services. The goal of this study is to identify the impact of AI techniques for customer rating to the products. We compared customer reviews of other portable speakers without AI features and those of a smart speaker. Amazon echo is used for a smart speaker and JBL Flip 4 Bluetooth Speaker and Ultimate Ears BOOM 2 Panther Limited Edition are used for the comparison. These products are in the same price range ($50~100) and selected as featured products in Amazon.com. All reviews for the products were collected and common words for all products and unique words of the smart speaker were identified. Information gain values were calculated to identify the influences of words to be rated as positive or negative. Positive and negative words in all the products or in Amazon echo were identified, too. Topic modeling was applied to the customer reviews on Amazon echo and the importance of each topic were measured by summating information gain values of each topic. This study provides a way of identifying customer responses on the AI feature and measuring the importance of the feature among diverse features of the products.

• Journal of Korean Biological Nursing Science
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• 제23권3호
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• pp.170-179
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Diabetes mellitus and mobile applications' field of research for better understanding research trends in the past 20 years. Methods: This study was a text-mining and topic modeling study including four steps such as 'collecting abstracts', 'extracting and cleaning semantic morphemes', 'building a co-occurrence matrix', and 'analyzing network features and clustering topic groups'. Results: A total of 789 papers published between 2002 and 2021 were found in databases (Springer). Among them, 435 words were extracted from 118 articles selected according to the conditions: 'analyzed by text network analysis and topic modeling'. The core keywords were 'self-management', 'intervention', 'health', 'support', 'technique' and 'system'. Through the topic modeling analysis, four themes were derived: 'intervention', 'blood glucose level control', 'self-management' and 'mobile health'. The main topic of this study was 'self-management'. Conclusion: While more recent work has investigated mobile applications, the highest feature was related to self-management in the diabetes care and prevention. Nursing interventions utilizing mobile application are expected to not only effective and powerful glycemic control and self-management tools, but can be also used for patient-driven lifestyle modification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2093-2108
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• 2017

In this paper, we describe a novel method for recognizing human actions from different views via view knowledge transfer. Our approach is characterized by two aspects: 1) We propose a unsupervised topic transfer model (TTM) to model two view-dependent vocabularies, where the original bag of visual words (BoVW) representation can be transferred into a bag of topics (BoT) representation. The higher-level BoT features, which can be shared across views, can connect action models for different views. 2) Our features make it possible to obtain a discriminative model of action under one view and categorize actions in another view. We tested our approach on the IXMAS data set, and the results are promising, given such a simple approach. In addition, we also demonstrate a supervised topic transfer model (STTM), which can combine transfer feature learning and discriminative classifier learning into one framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1442-1453
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• 2016

Now, there are prodigiously expanding worldwide economic networks in the information society, which require their social structural changes through technology innovations. This paper so tries to formally define a process-centered knowledge model to be used to analyze policy-making procedures on technology innovations. The eventual goal of the proposed knowledge model is to apply itself to analyze a topic network based upon composite keywords from a document written in a natural language format during the technology innovation procedures. Knowledge model is created to topic network that compositing driven keyword through text mining from natural language in document. And we show that the way of analyzing knowledge model and automatically generating feature keyword and relation properties into topic networks.