Although Pseudo-Relevance Feedback (PRF) is a widely used technique for enhancing average retrieval performance, it may actually hurt performance for around one-third of a given set of topics. To enhance the reliability of PRF, Flexible PRF has been proposed, which adjusts the number of pseudo-relevant documents and/or the number of expansion terms for each topic. This paper explores a new, inexpensive Flexible PRF method, called Selective Sampling, which is unique in that it can skip documents in the initial ranked output to look for more “novel” pseudo-relevant documents. While Selective Sampling is only comparable to Traditional PRF in terms of average performance and reliability, per-topic analyses show that Selective Sampling outperforms Traditional PRF almost as often as Traditional PRF outperforms Selective Sampling. Thus, treating the topPdocuments as relevant is often not the best strategy. However, predictingwhenSelective Sampling outperforms Traditional PRF appears to be as difficult as predicting when a PRF method fails. For example, our per-topic analyses show that even the proportion of truly relevant documents in the pseudo-relevant set is not necessarily a good performance predictor.

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We propose a non-interactive method for solving the contradictions caused by exceptions to ordinary rules. It is realized by: detecting the ordinary rules which have the instances in their bodies; and converting them into the default rules. To reduce the default rules which need much reasoning time, we convert the minimal sets of ordinary rules to solve contradictions. Since the proposed method is executed without human interaction, it contributes to automatic rule-base maintenance.