A probabilistic quantifier fuzzification mechanism:: The model and its evaluation for information retrieval

In this paper, we propose a new quantifier fuzzification mechanism which is deeply rooted in the theory of probability. This quantifier fuzzification mechanism skips the nested assumption, which is inherent to other probabilistic quantification methods. The new quantification approach complies with the properties required for determiner fuzzification schemes (DFS) with finite sets and, hence, its good behavior is assured. Moreover, this new approach is suitable for some application domains. In particular, the use of fuzzy quantifiers for implementing query quantified statements for information retrieval exemplifies the adequacy of the new proposal. The new quantifier fuzzification mechanism has been efficiently implemented and empirically tested for a retrieval task. This practical evaluation followed the standard methodology in the field of information retrieval and was conducted against a popular benchmark consisting of a large collection of documents. The retrieval performance evaluation made evident that: 1) the new method can work in realistic scenarios, and 2) it can overcome recent proposals for applying fuzzy quantifiers in information retrieval.