Retrieval models are the core components of information retrieval systems, which guide the document and query representations, as well as the document ranking schemes. TF-IDF, binary independence retrieval (BIR) model and language modelling (LM) are three of the most influential contemporary models due to their stability and performance. The BIR model and LM have probabilistic theory as their b...

We present the notion of Ranking for evaluation of two-class classifiers. Ranking is based on using the ordering information contained in the output of a scoring model, rather than just setting a classification threshold. Using this ordering information, we can evaluate the model's performance with regard to complex goal functions, such as the cor rect identification of the k most likely and/or...

Analysis of ranking data is often required in various fields of study, for example politics, market research and psychology. Over the years, many statistical models for ranking data have been developed. Among them, distance-based ranking models postulate that the probability of observing a ranking of items depends on the distance between the observed ranking and a modal ranking. The closer to t...

In this paper, we present our approach in the We Want Web(WWW)[1] task of NTCIR-13, for both English and Chinese languages. We implement a ranking model for traditional re-ranking problems based on learning to rank. We first process the raw data and extract text features, match features, embedding features and semantic features for each query-document pair. Then we use LamdaMART[2] to train the...