Enterprise intranets are often sparse in nature, with limited use of alternative lexical representations between authors, making query expansion (QE) ineffective. Hence, for some enterprise search queries, it can be advantageous to instead use the well-known collection enrichment (CE) method to gather higher quality pseudo-feedback documents from a more diverse external resource. However, it is not always clear for which queries the collection enrichment technique should be applied. In this paper, we study two different approaches, namely a predictor-based approach and a divergence-based approach, to decide on when to apply CE. We thoroughly evaluate both approaches on the TREC Enterprise track CERC test collection and its corresponding topic sets, in combination with three different external resources and nine different query performance predictors. Our results show that both approaches are effective to selectively apply CE for enterprise search. In particular, the divergence-based approach leads to consistent and marked retrieval improvements over the systematic application of QE or CE on all external resources.

Current blog opinion retrieval approaches cannot be applied if the topic relevance and opinion score distributions by rank are dissimilar. This problem severely limits the feasibility of these approaches. We propose to tackle this problem by fitting the distribution of opinion scores, which replaces the original topic relevance score distribution with the simulated one. Our proposed score distribution fitting method markedly enhances the feasibility of a state-of-the-art dictionary-based opinion retrieval approach. Evaluation on a standard TREC blog test collection shows significant improvements over high quality topic relevance baselines.

Document prior features, such as Pagerank and URL depth, can improve the retrieval effectiveness of Web Information Retrieval (IR) systems. However, not all queries equally benefit from the application of a document prior feature. This paper aims to investigate whether the retrieval performance can be further enhanced by selecting the best document prior feature on a per-query basis. We present a novel method for selecting the best document prior feature on a per-query basis. We evaluate our technique on the TREC .GOV Web test collection and its associated TREC 2003 Web search tasks. Our experiments demonstrate the effectiveness and robustness of our proposed selection method.

Term dependency, or co-occurrence, has been studied in language modelling, for instance by Metzler & Croft who showed that retrieval performance could be significantlyenhanced using term dependency information. In this work, weshow how term dependency can be modelled within the Divergence From Randomness (DFR) framework. We evaluate our term dependency model on the two adhoc retrieval tasks using the TREC .GOV2 Terabyte collection. Furthermore, we examine the effect of varying the term dependency window size on the retrieval performance of the proposed model. Our experiments show that term dependency can indeed besuccessfully incorporated within the DFR framework.