Scaling question answering to the web
ACM Transactions on Information Systems (TOIS)
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
Empirical Study on Collaborative Writing: What Do Co-authors Do, Use, and Like?
Computer Supported Cooperative Work
Co-authoring with structured annotations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Local Graph Partitioning using PageRank Vectors
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Adaptable and Adaptive Hypermedia Systems
Adaptable and Adaptive Hypermedia Systems
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
TextRunner: open information extraction on the web
NAACL-Demonstrations '07 Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
WikiWalk: random walks on Wikipedia for semantic relatedness
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
The Shallows: What the Internet Is Doing to Our Brains
The Shallows: What the Internet Is Doing to Our Brains
Enriching textbooks through data mining
Proceedings of the First ACM Symposium on Computing for Development
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The growth of open-access technical publications and other open-domain textual information sources means that there is an increasing amount of online technical material that is in principle available to all, but in practice, incomprehensible to most. We propose to address the task of helping readers comprehend complex technical material, by using statistical methods to model the "prerequisite structure" of a corpus --- i.e., the semantic impact of documents on an individual reader's state of knowledge. Experimental results using Wikipedia as the corpus suggest that this task can be approached by crowd-sourcing the production of ground-truth labels regarding prerequisite structure, and then generalizing these labels using a learned classifier which combines signals of various sorts. The features that we consider relate pairs of pages by analyzing not only textual features of the pages, but also how the containing corpora is connected and created.