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Investigating Weak Supervision in Deep Ranking

1 Introduction Document ranking is one of the core problems in information retrieval studies. Given a textual query, the goal of document ranking is to find relevant documents with respect to the query in the whole collection. Recently, researchers in the Information Retrieval(IR) community have proposed a number of neural ranking models to improve the performance of document ranking. However, the success of deep neural networks has not been widely observed in ad hoc retrieval ( Pang, Lan, Guo, Xu, & Cheng, 2017a ). One of the reasons lies in the shortage of

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To Phrase or Not to Phrase – Impact of User versus System Term Dependence upon Retrieval

general, the last study recording how users specified term dependence was from 2005 ( Jansen, Spink, & Pedersen, 2005 ). On the contrary, algorithmic approaches to detect and process term dependence have been explored much more in IR, for instance in ad-hoc retrieval ( Lioma & van Rijsbergen, 2008 ), patent retrieval ( Jochim, Lioma, & Schütze, 2011 ), domain-specific retrieval on physics academic literature ( Lioma, Kothari, & Schuetze, 2011 ), or more formally using logic ( Lioma, Larsen, Schütze, & Ingwersen, 2010 ). A recent comprehensive overview is given in

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