A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
A ranking approach to target detection for automatic link generation
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Visual exploration of health information for children
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
An evaluation framework for cross-lingual link discovery
Information Processing and Management: an International Journal
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We describe our participation in the Link-the-Wiki track at INEX 2009. We apply machine learning methods to the anchor-to-best-entry-point task and explore the impact of the following aspects of our approaches: features, learning methods as well as the collection used for training the models. We find that a learning to rank-based approach and a binary classification approach do not differ a lot. The new Wikipedia collection which is of larger size and which has more links than the collection previously used, provides better training material for learning our models. In addition, a heuristic run which combines the two intuitively most useful features outperforms machine learning based runs, which suggests that a further analysis and selection of features is necessary.