Guangyou Zhou

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Guangyou Zhou is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Determining relation semantics by mapping relation phrases to knowledge base Open Information Extraction
Relation Mapping
Wikipedia Infobox
Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 English 2013 0 0
Detecting Wikipedia vandalism with a contributing efficiency-based approach Classification
Vandalism detection
Lecture Notes in Computer Science English 2012 The collaborative nature of wiki has distinguished Wikipedia as an online encyclopedia but also makes the open contents vulnerable against vandalism. The current vandalism detection methods relying on basic statistic language features work well for explicitly offensive edits that perform massive changes. However, these techniques are evadable for the elusive vandal edits which make only a few unproductive or dishonest modifications. In this paper we proposed a contributing efficiency-based approach to detect the vandalism in Wikipedia and implement it with machine-learning based classifiers that incorporate the contributing efficiency along with other languages features. The results of extensional experiment show that the contributing efficiency can improve the recall of machine learning-based vandalism detection algorithms significantly. 0 0
Exploring the existing category hierarchy to automatically label the newly-arising topics in cQA Category hierarchy
Community question answering
Newly-arising topics
ACM International Conference Proceeding Series English 2012 This work investigates selecting concise labels for the newly-arising topics in community question answer. Previous methods of generating labels do not take the information of the existing category hierarchy into consideration. The main motivation of our paper is to utilize this information into the label generation process. We propose a general framework to address this problem. Firstly, we map the questions into Wikipedia concept sets, which are more meaningful than terms. Secondly, important concepts are identified to represent the main focus of the newly-arising topics. Thirdly, candidate labels are extracted from Wikipedia category graph. Finally, candidate labels are filtered and reranked by combination of structure information of existing category hierarchy and Wikipedia category graph. The experiments show that in our test collections, about 80% "correct" labels appear in the top ten labels recommended by our system. 0 0
Large-scale question classification in cQA by leveraging Wikipedia semantic knowledge Large-scale classification
Question retrieval
Translation model
CIKM English 2011 0 0