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 Ngram. 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. Categories and Subject Descriptors: H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Information Search and Retrieval; H.3.4 Systems and Software General Terms: Algorithms, Experimentation

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Jiyin He, Maarten de Rijke
• Keywords: Binary Classification, Binary Classification Approaches, Information Management, Relevant Wikipedia Concepts, SIGIR 2010