Yu Suzuki

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Yu Suzuki is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Assessing quality score of wikipedia articles using mutual evaluation of editors and texts Edit history
Peer review
Quality
Vandalism
Wikipedia
International Conference on Information and Knowledge Management, Proceedings English 2013 In this paper, we propose a method for assessing quality scores of Wikipedia articles by mutually evaluating editors and texts. Survival ratio based approach is a major approach to assessing article quality. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality, because poor quality texts have a high probability of being deleted by editors. However, many vandals, low quality editors, delete good quality texts frequently, which improperly decreases the survival ratios of good quality texts. As a result, many good quality texts are unfairly assessed as poor quality. In our method, we consider editor quality score for calculating text quality score, and decrease the impact on text quality by vandals. Using this improvement, the accuracy of the text quality score should be improved. However, an inherent problem with this idea is that the editor quality scores are calculated by the text quality scores. To solve this problem, we mutually calculate the editor and text quality scores until they converge. In this paper, we prove that the text quality score converges. We did our experimental evaluation, and confirmed that our proposed method could accurately assess the text quality scores. Copyright is held by the owner/author(s). 0 0
Clustering editors of wikipedia by editor's biases Bias
Edit histories
Peer reviews
Wikipedia
Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013 English 2013 Wikipedia is an Internet encyclopedia where any user can edit articles. Because editors act on their own judgments, editors' biases are reflected in edit actions. When editors' biases are reflected in articles, the articles should have low credibility. However, it is difficult for users to judge which parts in articles have biases. In this paper, we propose a method of clustering editors by editors' biases for the purpose that we distinguish texts' biases by using editors' biases and aid users to judge the credibility of each description. If each text is distinguished such as by colors, users can utilize it for the judgments of the text credibility. Our system makes use of the relationships between editors: agreement and disagreement. We assume that editors leave texts written by editors that they agree with, and delete texts written by editors that they disagree with. In addition, we can consider that editors who agree with each other have similar biases, and editors who disagree with each other have different biases. Hence, the relationships between editors enable to classify editors by biases. In experimental evaluation, we verify that our proposed method is useful in clustering editors by biases. Additionally, we validate that considering the dependency between editors improves the clustering performance. 0 0
Complementary information for Wikipedia by comparing multilingual articles Lecture Notes in Computer Science English 2013 Information of many articles is lacking in Wikipedia because users can create and edit the information freely. We specifically examined the multilinguality of Wikipedia and proposed a method to complement information of articles which lack information based on comparing different language articles that have similar contents. However, much non-complementary information is unrelated to a user's browsing article in the results. Herein, we propose improvement of the comparison area based on the classified complementary target. 0 0
Effects of implicit positive ratings for quality assessment of Wikipedia articles Edit history
Quality
Reputation
Wikipedia
Journal of Information Processing English 2013 In this paper, we propose a method to identify high-quality Wikipedia articles by using implicit positive ratings. One of the major approaches for assessing Wikipedia articles is a text survival ratio based approach. In this approach, when a text survives beyond multiple edits, the text is assessed as high quality. However, the problem is that many low quality articles are misjudged as high quality, because every editor does not always read the whole article. If there is a low quality text at the bottom of a long article, and the text has not seen by the other editors, then the text survives beyond many edits, and the text is assessed as high quality. To solve this problem, we use a section and a paragraph as a unit instead of a whole page. In our method, if an editor edits an article, the system considers that the editor gives positive ratings to the section or the paragraph that the editor edits. From experimental evaluation, we confirmed that the proposed method could improve the accuracy of quality values for articles. 0 0
Extracting complementary information from Wikipedia articles of different languages Comparison
Complementary information
Wikipedia
International Journal of Business Intelligence and Data Mining English 2013 In Wikipedia, users can create and edit information freely. Few editors take responsibility for editing the articles. Therefore, information of many Wikipedia articles is lacking. Furthermore, Wikipedia has different levels of value of its information depending on the language version of the site. In this paper, we propose the extraction of complementary information from different language Wikipedia and its automatic presentation. The important points of our method are: 1) extraction of comparison articles from different language Wikipedia; 2) extraction of complementary information; 3) presentation of complementary information. 0 0
Mutual Evaluation of Editors and Texts for Assessing Quality of Wikipedia Articles Wikipedia
Quality
Peer review
Edit history
Link analysis
WikiSym English August 2012 In this paper, we propose a method to identify good quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing article quality is a text survival ratio based approach. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality. This approach assumes that poor quality texts are deleted by editors with high possibility. However, many vandals delete good quality texts frequently, then the survival ratios of good quality texts are improperly decreased by vandals. As a result, many good quality texts are unfairly assessed as poor quality. In our method, we consider editor quality for calculating text quality, and decrease the impacts on text qualities by the vandals who has low quality. Using this improvement, the accuracy of the text quality should be improved. However, an inherent problem of this idea is that the editor qualities are calculated by the text qualities. To solve this problem, we mutually calculate the editor and text qualities until they converge. We did our experimental evaluation, and we confirmed that the proposed method could accurately assess the text qualities. 0 0
Assessing quality values of Wikipedia articles using implicit positive and negative ratings Edit history
Quality
Reputation
Wikipedia
Lecture Notes in Computer Science English 2012 In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and text using implicit positive and negative ratings. One of major approaches for assessing Wikipedia articles is a text survival ratio based approach. However, the problem of this approach is that many low quality articles are misjudged as high quality, because of two issues. This is because, every editor does not always read the whole articles. Therefore, if there is a low quality text at the bottom of a long article, and the text have not seen by the other editors, then the text survives beyond many edits, and the survival ratio of the text is high. To solve this problem, we use a section or a paragraph as a unit of remaining instead of a whole page. This means that if an editor edits an article, the system treats that the editor gives positive ratings to the section or the paragraph that the editor edits. This is because, we believe that if editors edit articles, the editors may not read the whole page, but the editors should read the whole sections or paragraphs, and delete low-quality texts. From experimental evaluation, we confirmed that the proposed method could improve the accuracy of quality values for articles. 0 0
Extracting difference information from multilingual wikipedia Lecture Notes in Computer Science English 2012 Wikipedia articles for a particular topic are written in many languages. When we select two articles which are about a single topic but which are written in different languages, the contents of these two articles are expected to be identical because of the Wikipedia policy. However, these contents are actually different, especially topics related to culture. In this paper, we propose a system to extract different Wikipedia information between that shown for Japan and that of other countries. An important technical problem is how to extract comparison target articles of Wikipedia. A Wikipedia article is written in different languages, with their respective linguistic structures. For example, "Cricket" is an important part of English culture, but the Japanese Wikipedia article related to cricket is too simple. Actually, it is only a single page. In contrast, the English version is substantial. It includes multiple pages. For that reason, we must consider which articles can be reasonably compared. Subsequently, we extract comparison target articles of Wikipedia based on a link graph and article structure. We implement our proposed method, and confirm the accuracy of difference extraction methods. 0 0
Extracting lack of information on Wikipedia by comparing multilingual articles ACM International Conference Proceeding Series English 2012 Wikipedia has multilingual articles, the information of which differs, even for articles on the same topic. As described in this paper, we propose a system to extract and present lack of information of one language on Wikipedia by comparing two languages on the Wikipedia. When we compare Wikipedia articles of two languages, the granularity of information between them differs. Therefore, we propose a method of extracting multiple comparison articles using a Wikipedia link graph. The system extracts lack of information that is included in articles in Wikipedia by comparing one base article with other articles that are found using the link graph. 0 0
Good quality complementary information for multilingual Wikipedia Lecture Notes in Computer Science English 2012 Many Wikipedia articles lack information, because not all users submit truly complete information to Wikipedia. However, Wikipedia has many language versions that have been developed independently. Therefore, if we supply these complementary information from many language versions, the users must satisfy the amount of information of Wikipedia articles with the complementary information, instead of only one language version of Wikipedia articles. In this study, we specifically examine multilingual Wikipedia and propose a method of extracting good quality complementary information from Wikipedia of other languages. Specifically, we compare Wikipedia articles with less information to those with more information. From Wikipedia articles, which can have the same theme and different languages, we extract different information as complementary information. As described herein, we extract comparison target articles of Wikipedia based on a link graph, because cases exist in which information included in an articles is written in multiple pages of different languages. Furthermore, some low-quality information is extracted as complementary information because Wikipedia articles are written by not only good editors but also bad editors such as vandals. We propose a method to calculate the quality of information based on the editors, and we extract good quality complementary information. 0 0
Mutual evaluation of editors and texts for assessing quality of Wikipedia articles Edit history
Link analysis
Peer review
Quality
Wikipedia
WikiSym 2012 English 2012 In this paper, we propose a method to identify good quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing article quality is a text survival ratio based approach. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality. This approach assumes that poor quality texts are deleted by editors with high possibility. However, many vandals delete good quality texts frequently, then the survival ratios of good quality texts are improperly decreased by vandals. As a result, many good quality texts are unfairly assessed as poor quality. In our method, we consider editor quality for calculating text quality, and decrease the impacts on text qualities by the vandals who has low quality. Using this improvement, the accuracy of the text quality should be improved. However, an inherent problem of this idea is that the editor qualities are calculated by the text qualities. To solve this problem, we mutually calculate the editor and text qualities until they converge. We did our experimental evaluation, and we confirmed that the proposed method could accurately assess the text qualities. 0 0
QualityRank: Assessing quality of wikipedia articles by mutually evaluating editors and texts Edit history
Link analysis
Peer review
Quality
HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media English 2012 In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing articles using edit history is a text survival ratio based approach. However, the problem is that many high-quality articles are identified as low quality, because many vandals delete high-quality texts, then the survival ratios of high-quality texts are decreased by vandals. Our approach's strongest point is its resistance to vandalism. Using our method, if we calculate text quality values using editor quality values, vandals do not affect any quality values of the other editors, then the accuracy of text quality values should improve. However, the problem is that editor quality values are calculated by text quality values, and text quality values are calculated by editor quality values. To solve this problem, we mutually calculate editor and text quality values until they converge. Using this method, we can calculate a quality value of a text that takes into consideration that of its editors. From experimental evaluation, we confirmed that the proposed method can improve the accuracy of quality values for articles. Copyright 2012 ACM. 0 0
Segmentation of review texts by using thesaurus and corpus-based word similarity Reviews
Text segmentation
Thesaurus
Wikipedia
KEOD 2012 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development English 2012 Recently, we can refer to user reviews in the shopping or hotel reservation sites. However, with the exponential growth of information of the Internet, it is becoming increasingly difficult for a user to read and understand all the materials from a large-scale reviews that is potentially of interest. In this paper, we propose a method for review texts segmentation by guest's criteria, such as service, location and facilities. Our system firstly extracts words which represent criteria from hotel review texts. We focused on topic markers such as "ha" in Japanese to extract guest's criteria. The extracted words are classified into classes with similar words. The classification is proceeded by using Japanese WordNet. Then, for each hotel, each text with all of the guest reviews is segmented into word sequence by using criteria classes. Review text segmentation is difficult because of short text. We thus used Japanese WordNet, extracted similar word pairs, and indexes of Wikipedia. We performed text segmentation of hotel review. The results showed the effectiveness of our method and indicated that it can be used for review summarization by guest's criteria. 0 0
Credibility Assessment Using Wikipedia for Messages on Social Network Services Credibility
Social Network Service
Wikipedia
DASC English 2011 0 0
Gist of a Thread in Social Network Services Based on Credibility of Wikipedia HICSS English 2011 0 0
Gist of a thread in social network services based on credibility of Wikipedia Proceedings of the Annual Hawaii International Conference on System Sciences English 2011 Users of Social Network Services(SNS) can sometimes enter into heated discussions, which prompt those users to concentrate on a single issue and lose track of the actual theme. We believe that it would be beneficial for users and visitors to present information to help understand the gist of the discussion at a glance. As described in this paper, we propose a system that presents a gist of a thread on an SNS and basic information about it by comparing the comments in the thread with Wikipedia article. Wikipedia articles, however, are not always credible. When we compare a thread on an SNS with Wikipedia, the Wikipeida article must have credible content. We measure the credibility of the article based on the credibility of Editors. We first extract the target passage which is candidate of the gist of a thread in an SNS based on the Wikipedia Table of Contents(TOC). Then we measure the credibility of editors of Wikipedia using the edit history and measure the credibility of the article using results of the credibility of editors. The target passage, which has a high similarity degree with comment in an SNS and has a high credibility rate becomes the gist of the thread in SNS. Consequently, users and viewers can ascertain the gist of an SNS thread by viewing a Wikipedia TOC with credibility. 0 0