A New Strategy to Seed Selection for the High Recall Task

High Recall Information Retrieval (HIRE) aims at identifying all (or nearly all) relevant documents given a query. HIRE, for example, is used in the systematic literature review task, where the goal is to identify all relevant scientific articles. The documents selected by HIRE as relevant define the user effort to identify the target information. On this way, one of HIRE goals is only to return relevant documents avoiding overburning the user with non-relevant information. Traditionally, supervised machine learning algorithms are used as HIRE' core to produce a ranking of relevant documents (e.g. SVM). However, such algorithms depend on an initial training set (seed) to start the process of learning. In this work, we propose a new approach to produce the initial seed for HIRE focus on reducing the user effort. Our approach combines an active learning approach with a raking strategy to select only the informative examples. The experimentation shows that our approach is able to reduce until 18% the labeling effort with competitive recall.