• Asia Marketing Journal
• /
• v.16 no.3
• /
• pp.23-33
• /
• 2014

The selection of keywords for bidding is a critical component of paid search advertising. When the number of possible keywords is enormous, it becomes difficult to choose the best keywords for advertising and then subsequently to assess their effect. To this end, we propose an ultrahigh dimensional keyword selection approach that not only reduces the dimension for selections, but also generates the top listed keywords for profits. An empirical analysis using a unique panel dataset from a large online clothes retailer that advertises on the largest search engine in China (i.e., Baidu) is presented to illustrate the usefulness of our approach.

• Proceedings of the KSPS conference
• /
• 2002.11a
• /
• pp.183-186
• /
• 2002

The speech detection is one of the important problems in real-time speech recognition. The accurate detection of speech boundaries is crucial to the performance of speech recognizer. In this paper, we propose a speech detector based on Mel-band selection through training. In order to show the excellence of the proposed algorithm, we compare it with a conventional one, so called, EPD-VAA (EndPoint Detector based on Voice Activity Detection). The proposed speech detector is trained in order to better extract keyword speech than other speech. EPD-VAA usually works well in high SNR but it doesn't work well any more in low SNR. But the proposed algorithm pre-selects useful bands through keyword training and decides the speech boundary according to the energy level of the sub-bands that is previously selected. The experimental result shows that the proposed algorithm outperforms the EPD-VAA.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.960-968
• /
• 2018

The mobile visual search service uses a query image to acquire linkage information through pre-constructed DB search. From the standpoint of this purpose, it would be more useful if you could perform a search on a web-based keyword search system instead of a pre-built DB search. In this paper, we propose a representative query extraction algorithm to be used as a keyword on a web-based search system. To do this, we use image classification labels generated by the CNN (Convolutional Neural Network) algorithm based on Deep Learning, which has a remarkable performance in image recognition. In the query extraction algorithm, dictionary meaningful words are extracted using Wikipedia, and hierarchical categories are constructed using WordNet. The performance of the proposed algorithm is evaluated by measuring the system response time.

• The Journal of Society for e-Business Studies
• /
• v.23 no.2
• /
• pp.83-96
• /
• 2018

Extracting keywords representing documents is very important because it can be used for automated services such as document search, classification, recommendation system as well as quickly transmitting document information. However, when extracting keywords based on the frequency of words appearing in a web site documents and graph algorithms based on the co-occurrence of words, the problem of containing various words that are not related to the topic potentially in the web page structure, There is a difficulty in extracting the semantic keyword due to the limit of the performance of the Korean tokenizer. In this paper, we propose a method to select candidate keywords based on semantic similarity, and solve the problem that semantic keyword can not be extracted and the accuracy of Korean tokenizer analysis is poor. Finally, we use the technique of extracting final semantic keywords through filtering process to remove inconsistent keywords. Experimental results through real web pages of small business show that the performance of the proposed method is improved by 34.52% over the statistical similarity based keyword selection technique. Therefore, it is confirmed that the performance of extracting keywords from documents is improved by considering semantic similarity between words and removing inconsistent keywords.

• Journal of Korean society of Dental Hygiene
• /
• v.21 no.5
• /
• pp.639-644
• /
• 2021

Objectives: The purpose of this study was to analyze the factors that affected the number of citations for articles published in the Journal of Korean Society of Dental Hygiene based on previous studies. Methods: Information on papers including the number of citations was collected using a web crawling technique. The effect of the number of author keywords, the number of Medical Subject Headings (MeSH) keywords, MeSH match rate, abstract word count and keyword-abstract ratio on the number of citations was analyzed by multiple regression analysis. Results: The use of the MeSH keyword did not have a significant effect on the number of citations. Among the other factors, only the keyword-abstract ratio was statistically significant. Conclusions: Select a topic of constant interest in the field, write the title in detail using colons or asterisks if necessary, and do not repeat the words used in the title in keywords. Select specific keywords deeply related to the topic. In particular, choice words or phrases that are frequently used in the abstract. If the MeSH keyword selection contradicts the previous strategies, boldly give up the MeSH keyword.

• Journal of Fashion Business
• /
• v.23 no.3
• /
• pp.51-66
• /
• 2019

The aim of this study is to identify research trends of "Journal of Fashion Business" by analyzing the keyword network of the paper published between 2006 and 2017. The papers selected for analysis in the study were 287 fashion design articles and 281 fashion marketing articles published between February 2006 and December 2017 and titles, volumes, publishing years, authors, keywords, and abstracts of each paper were collected for data analysis. The research was carried out through selection, collection of article data, keyword extraction and coding, keywords refinement, formation of network matrix, and analysis and visualization process. First, based on the title of the paper used in the analysis, the fashion design/aesthetics, marketing/social psychology, clothing materials, clothing composition, and other fields were classified. Research analysis used the Netminer 4 (Ver.4.3.2) program. Results indicated showed that the intellectual structure of the "Fashion Business" research paper showed key word changes over time, and the degree centrality and between centrality of the keywords.

• Journal of Information Technology Applications and Management
• /
• v.12 no.4
• /
• pp.45-57
• /
• 2005

In order to provide personalized knowledge recommendation services, most web portals for organizational knowledge management use category or keyword information that portal users explicitly express interests in. However, it is usually difficult to collect correct preference data for all users with this approach, and, moreover, users' preferences may easily change over time, which results In outdated user profiles and impaired recommendation qualify. In order to address this problem, this paper suggests knowledge recommendation methods for portals using user profiles that are automatically constructed from users' activities such as posting or uploading of articles and documents. The result of our experiment shows that the Proposed method can provide equivalent performance with the manual category or keyword selection method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.117-120
• /
• 2001

In this work, we propose a new method of extracting and weighting representative keywords(RKs) from a few documents that might interest a user. In order to extract RKs, we first extract candidate terms and then choose a number of terms called initial representative keywords (IRKS) from them through fuzzy inference. Then, by expanding and reweighting IRKS using term co-occurrence similarity, the final RKs are obtained. Performance of our approach is heavily influenced by effectiveness of selection method of IRKS so that we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of calculating center of document vectors. So, to show the usefulness of our approach, we compare with two famous methods - Rocchio and Widrow-Hoff - on a number of documents collections. The results show that our approach outperforms the other approaches.

• Journal of Korea Technology Innovation Society
• /
• v.8 no.2
• /
• pp.860-885
• /
• 2005

This study relates to develop a quantitative method through which promising technology-based business items can be discovered and selected. For this study, we utilized patent trend analysis, technology-industry concordance analysis, and keyword co-occurrence analysis of US patents. By analyzing patent trends and technology-industry concordance, we were able to find out the emerging industry trends : prevalence of bio industry, service industry, and B2C business. From the direct and co-occurrence analysis of newly discovered patent keywords in the year, 2000, 28 promising business item candidates were extracted. Finally, the promising item candidates were prioritized using 4 business attractiveness determinants; market size, product life cycle, degree of the technological innovation, and coincidence with the industry trends. This result implicates that reliable discovery and selection of promising technology-based business items can be performed by a quantitative, objective and low- cost process using knowledge discovery method from patent database instead of peer review.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.351-357
• /
• 2013

Information retrieval by query keywords have some mismatching problems to fit user's requirement for the retrieved documents due to the varieties of users. These problems are originated from the different situations and characteristics of user's requirement. Also, it has a problem that general correlation coefficients did not display the information relations. In this thesis, it is to suggest knowledge retrieval system to verify feasibility of personnel selection procedure and results supporting selection rules after construction of personnel selection ontologies and rules composed of various concept and knowledge based on the semantic web technology. In the suggested system, it is to clear disadvantages of limited information retrieval providing the suitable information to satisfy user's different situations and characteristics using Spearman's coefficients. Experimental results by this semantic-based information retrieval show 90.3% of accuracy and 71.8% of recall compared with legacy keyword information retrieval.