Ben Liu

From WikiPapers
Jump to: navigation, search

Ben Liu 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
Diversionary comments under political blog posts Coreference resolution
Diversionary comments
Extraction from wikipedia
Lda
Spam
Topic model
ACM International Conference Proceeding Series English 2012 An important issue that has been neglected so far is the identification of diversionary comments. Diversionary comments under political blog posts are defined as comments that deliberately twist the bloggers' intention and divert the topic to another one. The purpose is to distract readers from the original topic and draw attention to a new topic. Given that political blogs have significant impact on the society, we believe it is imperative to identify such comments. We then categorize diversionary comments into 5 types, and propose an effective technique to rank comments in descending order of being diversionary. To the best of our knowledge, the problem of detecting diversionary comments has not been studied so far. Our evaluation on 2,109 comments under 20 different blog posts from Digg.com shows that the proposed method achieves the high mean average precision (MAP) of 92.6%. Sensitivity analysis indicates that the effectiveness of the method is stable under different parameter settings. 0 0
Wikipedia as domain knowledge networks: Domain extraction and statistical measurement Betweenness centrality
Clustering coefficient
Degree distribution
Knowledge network
MathWorld
Power law
Statistics
Wikipedia
KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval English 2011 This paper investigates knowledge networks of specific domains extracted from Wikipedia and performs statistical measurements to selected domains. In particular, we first present an efficient method to extract a specific domain knowledge network from Wikipedia. We then extract four domain networks on, respectively, mathematics, physics, biology, and chemistry. We compare the mathematics domain network extracted from Wikipedia with MathWorld, the web's most extensive mathematical resource created and maintained by professional mathematicians, and show that they are statistically similar to each other. This indicates that Math- World and Wikipedia's mathematics domain knowledge share a similar internal structure. Such information may be useful for investigating knowledge networks. 0 0
Wiki-based Collaborative Learning: Incorporating Self-Assessment Tasks Computer assisted assessment
Formative assessment
Item model
Wiki-based collaborative learning
WikiSym English 2008 0 1
Wiki-based collaborative learning: Incorporating self-assessment tasks Computer assisted assess-ment
Formative assessment
Item model
Wiki-based collaborative learning
WikiSym 2008 - The 4th International Symposium on Wikis, Proceedings English 2008 When assigning technological articles as the collaborative writing task, students may find that the available knowledge repositories leave little room for them to contribute and therefore write nothing. To provide guidelines for students to discover topics, as well as tools to practice problem solving skills, we integrated a computer assisted assessment module into the Mediawiki and employ self-tests as the collaborative tasks. In these task, item models are used to automatically generate test questions. The items deriving from a same model share a common structure; however, the randomly initialized parameters of the model make them differ from each other. These differences result in that the answers of an item are usually inapplicable to other items deriving from the same model. Therefore, examinees have to solve these generated items on a case by case basis. Further, how to solve questions deriving from certain models can be served as the topics about which students write articles. The wiki self-assessment system was used in a course on Computer Networks offered to junior students majored in computer science. Five self-test tasks were assigned to 98 students, and they were encouraged to write wiki pages to explain their solution methods. Evidence from this preliminary application indicates that the presented approach has a positive effect on learning outcomes. 0 1