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With the expanding development of on-device artificial intelligence, voice-enabled devices such as smart speakers, wearables, and other on-device or edge processing systems have been proposed. However, building or obtaining large training datasets that are essential for robust keyword spotting (KWS) remains cumbersome. To address this problem, we propose a deep neural network that can rapidly establish a high-performance KWS system from arbitrary keyword instruction sets. We use an encoder pretrained with a large-scale speech corpus as the backbone network and then design an effective transfer network for KWS. To demonstrate the feasibility of the proposed network, various experiments were conducted on Google Speech Command Datasets V1 and V2. In addition, to verify the applicability of the network for different languages, we conducted experiments using three different Korean speech command datasets. The proposed network outperforms state-of-the-art deep neural networks in both experiments. Furthermore, the proposed network can understand real human voice even when trained with synthetic text-to-speech data.
Deokjin Seo; Heung-Seon Oh; Yuchul Jung. Wav2KWS: Transfer Learning From Speech Representations for Keyword Spotting. IEEE Access 2021, 9, 80682 -80691.
AMA StyleDeokjin Seo, Heung-Seon Oh, Yuchul Jung. Wav2KWS: Transfer Learning From Speech Representations for Keyword Spotting. IEEE Access. 2021; 9 (99):80682-80691.
Chicago/Turabian StyleDeokjin Seo; Heung-Seon Oh; Yuchul Jung. 2021. "Wav2KWS: Transfer Learning From Speech Representations for Keyword Spotting." IEEE Access 9, no. 99: 80682-80691.
Various websites have been created due to the increased use of the Internet, and the number of documents distributed through these websites has increased proportionally. However, it is not easy to collect newly updated documents rapidly. Web crawling methods have been used to continuously collect and manage new documents, whereas existing crawling systems applying a single node demonstrate limited performances. Furthermore, crawlers applying distribution methods exhibit a problem related to effective node management for crawling. This study proposes an efficient distributed crawler through stepwise crawling node allocation, which identifies websites" properties and establishes crawling policies based on the properties identified to collect a large number of documents from multiple websites. The proposed crawler can calculate the number of documents included in a website, compare data collection time and the amount of data collected based on the number of nodes allocated to a specific website by repeatedly visiting the website, and automatically allocate the optimal number of nodes to each website for crawling. An experiment is conducted where the proposed and single-node methods are applied to 12 different websites; the experimental result indicates that the proposed crawler"s data collection time decreased significantly compared with that of a single node crawler. This result is obtained because the proposed crawler applied data collection policies according to websites. Besides, it is confirmed that the work rate of the proposed model increased.
Hyuntae Kim; Junhyung Byun; Yoseph Na; Yuchul Jung. Implementation of Efficient Distributed Crawler through Stepwise Crawling Node Allocation. JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 2020, 10, 15 -31.
AMA StyleHyuntae Kim, Junhyung Byun, Yoseph Na, Yuchul Jung. Implementation of Efficient Distributed Crawler through Stepwise Crawling Node Allocation. JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE. 2020; 10 (2):15-31.
Chicago/Turabian StyleHyuntae Kim; Junhyung Byun; Yoseph Na; Yuchul Jung. 2020. "Implementation of Efficient Distributed Crawler through Stepwise Crawling Node Allocation." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 10, no. 2: 15-31.
Joo-Chan Park; Seon-Hoon Lee; Jun-Uk Jung; Sung-Bin Son; Heung-Seon Oh; Yuchul Jung. Uncertainty-based Deep Object Detection from Aerial Images. Journal of Institute of Control, Robotics and Systems 2020, 26, 891 -899.
AMA StyleJoo-Chan Park, Seon-Hoon Lee, Jun-Uk Jung, Sung-Bin Son, Heung-Seon Oh, Yuchul Jung. Uncertainty-based Deep Object Detection from Aerial Images. Journal of Institute of Control, Robotics and Systems. 2020; 26 (11):891-899.
Chicago/Turabian StyleJoo-Chan Park; Seon-Hoon Lee; Jun-Uk Jung; Sung-Bin Son; Heung-Seon Oh; Yuchul Jung. 2020. "Uncertainty-based Deep Object Detection from Aerial Images." Journal of Institute of Control, Robotics and Systems 26, no. 11: 891-899.
Despite extensive research, performance enhancement of keyphrase (KP) extraction remains a challenging problem in modern informatics. Recently, deep learning-based supervised approaches have exhibited state-of-the-art accuracies with respect to this problem, and several of the previously proposed methods utilize Bidirectional Encoder Representations from Transformers (BERT)-based language models. However, few studies have investigated the effective application of BERT-based fine-tuning techniques to the problem of KP extraction. In this paper, we consider the aforementioned problem in the context of scientific articles by investigating the fine-tuning characteristics of two distinct BERT models — BERT (i.e., base BERT model by Google) and SciBERT (i.e., a BERT model trained on scientific text). Three different datasets (WWW, KDD, and Inspec) comprising data obtained from the computer science domain are used to compare the results obtained by fine-tuning BERT and SciBERT in terms of KP extraction.
Yeonsoo Lim; Deokjin Seo; Yuchul Jung. Fine-tuning BERT Models for Keyphrase Extraction in Scientific Articles. JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 2020, 10, 45 -56.
AMA StyleYeonsoo Lim, Deokjin Seo, Yuchul Jung. Fine-tuning BERT Models for Keyphrase Extraction in Scientific Articles. JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE. 2020; 10 (1):45-56.
Chicago/Turabian StyleYeonsoo Lim; Deokjin Seo; Yuchul Jung. 2020. "Fine-tuning BERT Models for Keyphrase Extraction in Scientific Articles." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 10, no. 1: 45-56.
Keyphrase extraction is a fundamental, but very important task in NLP that map documents to a set of representative words/phrases. However, state-of-the-art results on benchmark datasets are still immature stage. As an effort to alleviate the gaps between human annotated keyphrases and automatically extracted ones, in this paper, we introduce our on-going work about how to extract meaningful keyphrases of scientific research articles. Moreover, we investigate several avenues of refining the extracted ones using pre-trained word embeddings and its variations. For the experiments, we use two different datasets (i.e., WWW and KDD) in computer science domain.
Yeonsoo Lim; Daehyeon Bong; Yuchul Jung. A Study on Automatic Keyphrase Extraction and Its Refinement for Scientific Articles. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 18 -21.
AMA StyleYeonsoo Lim, Daehyeon Bong, Yuchul Jung. A Study on Automatic Keyphrase Extraction and Its Refinement for Scientific Articles. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():18-21.
Chicago/Turabian StyleYeonsoo Lim; Daehyeon Bong; Yuchul Jung. 2020. "A Study on Automatic Keyphrase Extraction and Its Refinement for Scientific Articles." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 18-21.
As the size of the domestic and international gaming industry gradually grows, various games are undergoing rapid development cycles to compete in the current market. However, selecting and recommending suitable games for users continues to be a challenging problem. Although game recommendation systems based on the prior gaming experience of users exist, they are limited owing to the cold start problem. Unlike existing approaches, the current study addressed existing problems by identifying the personality of the user through a personality diagnostic test and mapping the personality to the player type. In addition, an Android app-based prototype was developed that recommends games by mapping tag information about the user’s personality and the game. A set of user experiments were conducted to verify the feasibility of the proposed mapping model and the recommendation prototype.
Yeonghun Lee; Yuchul Jung. A Mapping Approach to Identify Player Types for Game Recommendations. Information 2019, 10, 379 .
AMA StyleYeonghun Lee, Yuchul Jung. A Mapping Approach to Identify Player Types for Game Recommendations. Information. 2019; 10 (12):379.
Chicago/Turabian StyleYeonghun Lee; Yuchul Jung. 2019. "A Mapping Approach to Identify Player Types for Game Recommendations." Information 10, no. 12: 379.
One of the significant issues in a smart city is maintaining a healthy environment. To improve the environment, huge amounts of data are gathered, manipulated, analyzed, and utilized, and these data might include noise, uncertainty, or unexpected mistreatment of the data. In some datasets, the class imbalance problem skews the learning performance of the classification algorithms. In this paper, we propose a case-based reasoning method that combines the use of crowd knowledge from open source data and collective knowledge. This method mitigates the class imbalance issues resulting from datasets, which diagnose wellness levels in patients suffering from stress or depression. We investigate effective ways to mitigate class imbalance issues in which the datasets have a higher proportion of one class over another. The results of this proposed hybrid reasoning method, using a combination of crowd knowledge extracted from open source data (i.e., a Google search, or other publicly accessible source) and collective knowledge (i.e., case-based reasoning), were that it performs better than other traditional methods (e.g., SMO, BayesNet, IBk, Logistic, C4.5, and crowd reasoning). We also demonstrate that the use of open source and big data improves the classification performance when used in addition to conventional classification algorithms.
Ohbyung Kwon; Yun Seon Kim; Namyeon Lee; Yuchul Jung. When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning. Journal of Healthcare Engineering 2018, 2018, 1 -15.
AMA StyleOhbyung Kwon, Yun Seon Kim, Namyeon Lee, Yuchul Jung. When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning. Journal of Healthcare Engineering. 2018; 2018 ():1-15.
Chicago/Turabian StyleOhbyung Kwon; Yun Seon Kim; Namyeon Lee; Yuchul Jung. 2018. "When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning." Journal of Healthcare Engineering 2018, no. : 1-15.
Yuchul Jung; Jinyoung Kim; Heung-Seon Oh; Dongjun Suh; Jeong-Soo Kim; Seok-Hyong Lee; Kwang-Young Kim; Jungsun Yoon. Entity-Driven Knowledge Analytics Platform for Science and Technology. International Journal of Advanced Science and Technology 2018, 116, 1 -12.
AMA StyleYuchul Jung, Jinyoung Kim, Heung-Seon Oh, Dongjun Suh, Jeong-Soo Kim, Seok-Hyong Lee, Kwang-Young Kim, Jungsun Yoon. Entity-Driven Knowledge Analytics Platform for Science and Technology. International Journal of Advanced Science and Technology. 2018; 116 ():1-12.
Chicago/Turabian StyleYuchul Jung; Jinyoung Kim; Heung-Seon Oh; Dongjun Suh; Jeong-Soo Kim; Seok-Hyong Lee; Kwang-Young Kim; Jungsun Yoon. 2018. "Entity-Driven Knowledge Analytics Platform for Science and Technology." International Journal of Advanced Science and Technology 116, no. : 1-12.
With the recent advances of information and communication technology, people communicate with each other through online communities or social networking services, such as PatientsLikeMe and Facebook. One of the key challenges in aspects of providing sustainable situation-aware services is how to utilize peoples’ experiences shared as reusable social-intelligence. If domain-specific collective intelligence is well constructed, the knowledge usages can be extended to situation-awareness-based personal situation understanding, and sustainable recommendation services with user intent. In this paper, we introduce a sustainable situation-awareness supporting framework based on text-mining techniques and a domain-specific knowledge model, the so-called Service Quality Model for Hospitals (SQM-H). Different from obtaining sustainable contexts from heterogeneous sensors surrounding users, it aggregates SQM-H based service-specific knowledge from online health communities. Our framework includes a set of components: data aggregation, text-mining, service quality analysis, and open Application Programming Interface (APIs) for recommendation services. Those components have been designed to deal with users’ immediate request, providing service quality related information reflected in collective intelligence and analyzed information based on that along with the SQM-H. As a proof of concept, we implemented a prototype system which interacts with users through smartphone user interface. Our framework supports qualitative and quantitative information based on SQM-H and statistical analyses for the given user queries. Through the implementation and user tests, we confirmed an increased knowledge support for decision-making and an easy mashup with provided Open APIs. We believe that the suggested situation-awareness supporting framework can be applied to numerous sustainable applications related to healthcare and wellness domain areas if domain-specific knowledge models are redesigned.
Yuchul Jung; Cinyoung Hur; Mucheol Kim. Sustainable Situation-Aware Recommendation Services with Collective Intelligence. Sustainability 2018, 10, 1632 .
AMA StyleYuchul Jung, Cinyoung Hur, Mucheol Kim. Sustainable Situation-Aware Recommendation Services with Collective Intelligence. Sustainability. 2018; 10 (5):1632.
Chicago/Turabian StyleYuchul Jung; Cinyoung Hur; Mucheol Kim. 2018. "Sustainable Situation-Aware Recommendation Services with Collective Intelligence." Sustainability 10, no. 5: 1632.
Term mismatching between queries and documents has long been recognized as a key problem in information retrieval (IR). Based on our analysis of a large-scale web query log and relevant documents in standard test collections, we attempt to detect topic transitions between the topical categories of a query and those of relevant documents (or clicked pages) and create a Topic Transition Map (TTM) that captures how query topic categories are linked to those of relevant or clicked documents. TTM, a kind of click-graph at the semantic level, is then used for query expansion by suggesting the terms associated with the document categories strongly related to the query category. Unlike most other query expansion methods that attempt to either interpret the semantics of queries based on a thesaurus-like resource or use the content of a small number of relevant documents, our method proposes to retrieve documents in the semantic affinity of multiple categories of the documents relevant for the queries of a similar kind. Our experiments show that the proposed method is superior in effectiveness to other representative query expansion methods such as standard relevance feedback, pseudo relevance feedback, and thesaurus-based expansion of queries.
Kyung-Min Kim; Yuchul Jung; Sung-Hyon Myaeng. A Topic Transition Map for Query Expansion: A Semantic Analysis of Click-Through Data and Test Collections. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 648 -664.
AMA StyleKyung-Min Kim, Yuchul Jung, Sung-Hyon Myaeng. A Topic Transition Map for Query Expansion: A Semantic Analysis of Click-Through Data and Test Collections. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():648-664.
Chicago/Turabian StyleKyung-Min Kim; Yuchul Jung; Sung-Hyon Myaeng. 2016. "A Topic Transition Map for Query Expansion: A Semantic Analysis of Click-Through Data and Test Collections." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 648-664.
Yuchul Jung. A semantic annotation framework for scientific publications. Quality & Quantity 2016, 51, 1009 -1025.
AMA StyleYuchul Jung. A semantic annotation framework for scientific publications. Quality & Quantity. 2016; 51 (3):1009-1025.
Chicago/Turabian StyleYuchul Jung. 2016. "A semantic annotation framework for scientific publications." Quality & Quantity 51, no. 3: 1009-1025.
Display Omitted We propose a query expansion method which utilizes multiple external collections.To estimate each relevance model, we use the structure of the external collections.Our method extends queries effectively by considering related context information.Exhaustive experiments on three different medical test collections were performed.We report our lessons learned from dealing with different medical test collections. Utilizing external collections to improve retrieval performance is challenging research because various test collections are created for different purposes. Improving medical information retrieval has also gained much attention as various types of medical documents have become available to researchers ever since they started storing them in machine processable formats. In this paper, we propose an effective method of utilizing external collections based on the pseudo relevance feedback approach. Our method incorporates the structure of external collections in estimating individual components in the final feedback model. Extensive experiments on three medical collections (TREC CDS, CLEF eHealth, and OHSUMED) were performed, and the results were compared with a representative expansion approach utilizing the external collections to show the superiority of our method.
Heung-Seon Oh; Yuchul Jung. Cluster-based query expansion using external collections in medical information retrieval. Journal of Biomedical Informatics 2015, 58, 70 -79.
AMA StyleHeung-Seon Oh, Yuchul Jung. Cluster-based query expansion using external collections in medical information retrieval. Journal of Biomedical Informatics. 2015; 58 ():70-79.
Chicago/Turabian StyleHeung-Seon Oh; Yuchul Jung. 2015. "Cluster-based query expansion using external collections in medical information retrieval." Journal of Biomedical Informatics 58, no. : 70-79.
The widespread use of the Web has radically changed the way people acquire medical information. Every day, patients, their caregivers, and doctors themselves search for medical information to resolve their medical information needs. However, search results provided by existing medical search engines often contain irrelevant or uninformative documents that are not appropriate for the purposes of the users. As a solution, this paper presents a method of re-ranking medical documents. The key concept of our method is to compute accurate similarity scores through multiple stages of re-ranking documents from the initial documents retrieved by a search engine. Specifically, our method combines query expansion with abbreviations, query expansion with discharge summary, clustering-based document scoring, centrality-based document scoring, and pseudo relevance feedback with relevance model. The experimental results from participating in Task 3a of the CLEF 2014 eHealth show the performance of our method.
Heung-Seon Oh; Yuchul Jung; Kwang-Young Kim. A Multiple-Stage Approach to Re-ranking Medical Documents. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 166 -177.
AMA StyleHeung-Seon Oh, Yuchul Jung, Kwang-Young Kim. A Multiple-Stage Approach to Re-ranking Medical Documents. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():166-177.
Chicago/Turabian StyleHeung-Seon Oh; Yuchul Jung; Kwang-Young Kim. 2015. "A Multiple-Stage Approach to Re-ranking Medical Documents." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 166-177.
The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. We defined social media–based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea’s two biggest online portals were used to test the effectiveness of detection of social media–based key quality factors for hospitals. To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media–based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies.
Yuchul Jung; Cinyoung Hur; Dain Jung; Minki Kim; Lipeng Ning; Eyal Zimlichman. Identifying Key Hospital Service Quality Factors in Online Health Communities. Journal of Medical Internet Research 2015, 17, e90 .
AMA StyleYuchul Jung, Cinyoung Hur, Dain Jung, Minki Kim, Lipeng Ning, Eyal Zimlichman. Identifying Key Hospital Service Quality Factors in Online Health Communities. Journal of Medical Internet Research. 2015; 17 (4):e90.
Chicago/Turabian StyleYuchul Jung; Cinyoung Hur; Dain Jung; Minki Kim; Lipeng Ning; Eyal Zimlichman. 2015. "Identifying Key Hospital Service Quality Factors in Online Health Communities." Journal of Medical Internet Research 17, no. 4: e90.
A service mashup goes through several processes, which it takes much time and efforts for developers to mashup of many heterogeneous web services. To mitigate the complexity of a service mashup and automate the mashup process, the present paper proposes semantic service discovery and matching technologies. The semantic service discovery technology is capable of finding out more appropriate and ranked services with a given query, and the semantic service matching technology enables searching for compatible and interoperable services automatically across a number of heterogeneous web services. The semantic service discovery and matching technologies are based on the service ontology and service metadata that play important roles in relieving the semantic gap between a user's natural query and the technical service description. To verify the usability and effectiveness of the proposed technologies on this environment, experiments and simple use cases are shown. The results indicate that the proposed technologies help developers create new mashup applications more effectively and conveniently.
Yoo-Mi Park; Hyunkyung Yoo; Cinyoung Hur; Hyunjoo Bae; Yuchul Jung. Semantic service discovery and matching for semi-automatic service mashup. Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) 2015, 332 -337.
AMA StyleYoo-Mi Park, Hyunkyung Yoo, Cinyoung Hur, Hyunjoo Bae, Yuchul Jung. Semantic service discovery and matching for semi-automatic service mashup. Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015). 2015; ():332-337.
Chicago/Turabian StyleYoo-Mi Park; Hyunkyung Yoo; Cinyoung Hur; Hyunjoo Bae; Yuchul Jung. 2015. "Semantic service discovery and matching for semi-automatic service mashup." Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) , no. : 332-337.
Classifying documents to a large-scale web taxonomy is a challenging research problem because of a large number of categories and associated documents in the taxonomy. The state-of-the-art solution known as the narrow-down approach utilizes a search engine to reduce an entire category hierarchy to most relevant categories and selects the best one among them using a classifier. In a recent language modelling approach, top-level category information (or global information) was used in judging the appropriateness of a local category, which led to performance improvements. However, we observe that using global information has a limited influence on the final category selection under some conditions. First, global information may be inaccurate even though it is generated by a top-level category classifier using an entire hierarchy. Second, it has little influence when two competing categories share the same top-level category or when the local category information has too strong an influence on the final category selection. To resolve the limitations, in this paper, we propose two external methods: (1) a meta-classifier with novel dependency features among top-level categories based on an ensemble learning framework; and (2) a query modification model based on a statistical feedback method to improve the query document representation instead of just juggling with information in the hierarchy. Our methods were evaluated using the Open Directory Project test collection.
Heung-Seon Oh; Yuchul Jung. External methods to address limitations of using global information on the narrow-down approach for hierarchical text classification. Journal of Information Science 2014, 40, 688 -708.
AMA StyleHeung-Seon Oh, Yuchul Jung. External methods to address limitations of using global information on the narrow-down approach for hierarchical text classification. Journal of Information Science. 2014; 40 (5):688-708.
Chicago/Turabian StyleHeung-Seon Oh; Yuchul Jung. 2014. "External methods to address limitations of using global information on the narrow-down approach for hierarchical text classification." Journal of Information Science 40, no. 5: 688-708.
Health 2.0 is a benefit to society by helping patients acquire knowledge about health care by harnessing collective intelligence. However, any misleading information can directly affect patients’ choices of hospitals and drugs, and potentially exacerbate their health condition. This study investigates the congruence between crowdsourced information and official government data in the health care domain and identifies the determinants of low congruence where it exists. In-line with infodemiology, we suggest measures to help the patients in the regions vulnerable to inaccurate health information. We text-mined multiple online health communities in South Korea to construct the data for crowdsourced information on public health services (173,748 messages). Kendall tau and Spearman rank order correlation coefficients were used to compute the differences in 2 ranking systems of health care quality: actual government evaluations of 779 hospitals and mining results of geospecific online health communities. Then we estimated the effect of sociodemographic characteristics on the level of congruence by using an ordinary least squares regression. The regression results indicated that the standard deviation of married women’s education (P=.046), population density (P=.01), number of doctors per pediatric clinic (P=.048), and birthrate (P=.002) have a significant effect on the congruence of crowdsourced data (adjusted R 2=.33). Specifically, (1) the higher the birthrate in a given region, (2) the larger the variance in educational attainment, (3) the higher the population density, and (4) the greater the number of doctors per clinic, the more likely that crowdsourced information from online communities is congruent with official government data. To investigate the cause of the spread of misleading health information in the online world, we adopted a unique approach by associating mining results on hospitals from geospecific online health communities with the sociodemographic characteristics of corresponding regions. We found that the congruence of crowdsourced information on health care services varied across regions and that these variations could be explained by geospecific demographic factors. This finding can be helpful to governments in reducing the potential risk of misleading online information and the accompanying safety issues.
Minki Kim; Yuchul Jung; Dain Jung; Cinyoung Hur; Jinwook Choi; Hyojung Tak; Jooyoung Lee; Scott Erdley. Investigating the Congruence of Crowdsourced Information With Official Government Data: The Case of Pediatric Clinics. Journal of Medical Internet Research 2014, 16, e29 .
AMA StyleMinki Kim, Yuchul Jung, Dain Jung, Cinyoung Hur, Jinwook Choi, Hyojung Tak, Jooyoung Lee, Scott Erdley. Investigating the Congruence of Crowdsourced Information With Official Government Data: The Case of Pediatric Clinics. Journal of Medical Internet Research. 2014; 16 (2):e29.
Chicago/Turabian StyleMinki Kim; Yuchul Jung; Dain Jung; Cinyoung Hur; Jinwook Choi; Hyojung Tak; Jooyoung Lee; Scott Erdley. 2014. "Investigating the Congruence of Crowdsourced Information With Official Government Data: The Case of Pediatric Clinics." Journal of Medical Internet Research 16, no. 2: e29.
With the growing number of Web services available on the Web, finding services that match user's query becomes more difficult. To deal with the problem, numerous approaches employed semantic techniques in terms of annotation (or tagging) and service discovery algorithms. However, the problem of semantic service discovery based on the identification of fine-grained goals of users and services is still challenging due to the lack of semantic information in Web services. To enable the goal-driven semantic service discovery, we propose an automatic functional-goals tagging approach which employs a set of natural language processing (NLP) procedures based on the descriptions of Web services. In addition, to check the effectiveness of the functional-goals tagged, we designed a goal-driven semantic service discovery algorithm and compared it with other approaches: keyword-based, ontology-based, and topic-based service discovery. As results, our proposed goal-driven semantic service discovery achieved 75.7% of precision and 59.6% of recall (finally, F1=62%) that outperform other discovery approaches.
Yuchul Jung; Yoonsung Cho; Yoo-Mi Park; Taedong Lee. Automatic Tagging of Functional-Goals for Goal-Driven Semantic Service Discovery. 2013 IEEE Seventh International Conference on Semantic Computing 2013, 212 -219.
AMA StyleYuchul Jung, Yoonsung Cho, Yoo-Mi Park, Taedong Lee. Automatic Tagging of Functional-Goals for Goal-Driven Semantic Service Discovery. 2013 IEEE Seventh International Conference on Semantic Computing. 2013; ():212-219.
Chicago/Turabian StyleYuchul Jung; Yoonsung Cho; Yoo-Mi Park; Taedong Lee. 2013. "Automatic Tagging of Functional-Goals for Goal-Driven Semantic Service Discovery." 2013 IEEE Seventh International Conference on Semantic Computing , no. : 212-219.
Constructing a sophisticated experiential knowledge base for solving daily problems is essential for many intelligent human centric applications. A key issue is to convert natural language instructions into a form that can be searched semantically or processed by computer programs. This paper presents a methodology for automatically detecting actionable clauses in how-to instructions. In particular, this paper focuses on processing non-imperative clauses to elicit implicit instructions or commands. Based on some dominant linguistic styles in how-to instructions, we formulate the problem of detecting actionable clauses using linguistic features including syntactic and modal characteristics. The experimental results show that the features we have extracted are very promising in detecting actionable non-imperative clauses. This algorithm makes it possible to extract complete action sequences to a structural format for problem solving tasks.
JiHee Ryu; Yuchul Jung; Sung-Hyon Myaeng. Actionable Clause Detection from Non-imperative Sentences in Howto Instructions: A Step for Actionable Information Extraction. Computer Vision 2012, 7499, 272 -281.
AMA StyleJiHee Ryu, Yuchul Jung, Sung-Hyon Myaeng. Actionable Clause Detection from Non-imperative Sentences in Howto Instructions: A Step for Actionable Information Extraction. Computer Vision. 2012; 7499 ():272-281.
Chicago/Turabian StyleJiHee Ryu; Yuchul Jung; Sung-Hyon Myaeng. 2012. "Actionable Clause Detection from Non-imperative Sentences in Howto Instructions: A Step for Actionable Information Extraction." Computer Vision 7499, no. : 272-281.
Sung-Hyon Myaeng; Yoonjae Jeong; Yuchul Jung. Experiential Knowledge Mining. Foundations and Trends® in Web Science 2012, 4, 1 -102.
AMA StyleSung-Hyon Myaeng, Yoonjae Jeong, Yuchul Jung. Experiential Knowledge Mining. Foundations and Trends® in Web Science. 2012; 4 (1):1-102.
Chicago/Turabian StyleSung-Hyon Myaeng; Yoonjae Jeong; Yuchul Jung. 2012. "Experiential Knowledge Mining." Foundations and Trends® in Web Science 4, no. 1: 1-102.