This paper describes the system developed by the Language and Reasoning Group of UAM for the Relevance Feedback track of INEX 2012. The presented system focuses on the problem of ranking documents in accordance to their relevance. It is mainly based on the following hypotheses: (i) current IR machines are able to retrieve relevant documents for most of general queries, but they can not generate...

In this paper, we investigate a new method to handle multiple attribute group decision making (MAGDM) problems based on combined ranking value under interval type-2 fuzzy environment, in which all the attribute values provided by experts take the form of interval type-2 fuzzy sets (IT2FSs). We first introduce some basic concepts and related operational laws on IT2FSs. Then, we put forward three...

We describe our participation in the subtopic mining and document ranking subtasks of the NTCIR-9 Intent task, for both Chinese and Japanese languages. In the subtopic mining subtask, we experiment with a novel data-driven approach for ranking reformulations of an ambiguous query. In the document ranking subtask, we deploy our state-ofthe-art xQuAD framework for search result diversification.