A ranking approach to target detection for automatic link generation

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.