Non-relevance feedback document retrieval

This paper reports a new document retrieval method using nonrelevant documents. From a large data set of documents, we need to find documents that relate to human interesting in as few iterations of human testing or checking as possible. In each iteration a comparatively small batch of documents is evaluated for relating to the human interesting. We applied active learning techniques based on support vector machine for evaluating successive batches, which is called relevance feedback. Our proposed approach has been very useful for document retrieval with relevance feedback experimentally. The relevance feedback needs a set of relevant and nonrelevant documents to work usefully. However, the initial retrieved documents, which are displayed to a user, sometimes don\´t include relevant documents. In order to solve this problem, we propose a new feedback method using information of nonrelevant documents only. We named this method "nonrelevance feedback document retrieval". The nonrelevance feedback document retrieval is based on one-class support vector machine. Our experimental results show that this method can retrieve relevant documents using information of nonrelevant documents only.