A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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
An exploration of learning to link with Wikipedia: features, methods and training collection
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
Automatically embedding newsworthy links to articles
Proceedings of the 21st ACM international conference on Information and knowledge management
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We focus on the task of target detection in automatic link generation with Wikipedia, i.e., given an N-gram in a snippet of text, find the relevant Wikipedia concepts that explain or provide background knowledge for it. We formulate the task as a ranking problem and investigate the effectiveness of learning to rank approaches and of the features that we use to rank the target concepts for a given N-gram. Our experiments show that learning to rank approaches outperform traditional binary classification approaches. Also, our proposed features are effective both in binary classification and learning to rank settings.