Paper Info
Title
Supporting Contextualized Information Finding with Automatic Excerpt Categorization.
Abstract
The volume of information on the Web is constantly growing. Consequently, finding specific pieces of information becomes a harder task. Wikipedia, the largest online reference Website is beginning to witness this phenomenon. Learners often turn to Wikipedia in order to learn facts regarding different subjects. However, as time passes, Wikipedia articles get larger and specific information gets more difficult to be located. In this work, we propose an automatic annotation method that is able to precisely assign categories to any textual resource. Our approach relies on semantic enhanced annotations and the categorization schema of Wikipedia. The results of a user study show that our proposed method provides solid results for classifying text and provides a useful support for locating information. As implication, our research will help future learners to easily identify desired learning topics of interest in large textual resources.
Year
DOI
Venue
2014
10.1016/j.procs.2014.08.136
Procedia Computer Science
Keywords
Field
DocType
Annotation,Categorization,Wikipedia,Learning Support,
Categorization,Data mining,World Wide Web,Annotation,Information retrieval,Computer science,Knowledge-based systems,Witness,Specific-information,Phenomenon,Schema (psychology),Semantics
Conference
Volume
ISSN
Citations 
35
1877-0509
0
PageRank 
References 
Authors
0.34
15
3
Name
Order
Citations
PageRank
Ricardo Kawase11009.99
Patrick Siehndel212615.69
Bernardo Pereira Nunes318530.96