The Blog track explores the information seeking behaviour in the blogosphere. The track was introduced in 2006 [1], with a main pilot search task, namely the opinion-finding task. In TREC 2007 [2], the track investigated two main tasks inspired by the analysis of a

Abstract. Queries submitted to a retrieval system are often ambiguous. In such a situation, a sensible strategy is to diversify the ranking of results to be retrieved, in the hope that users will find at least one of these results to be relevant to their information need. In this paper, we introduce xQuAD, a novel framework for search result diversification that builds such a diversified ranking by explicitly accounting for the relationship between documents retrieved for the original query and the possible aspects underlying this query, in the form of sub-queries. We evaluate the effectiveness of xQuAD using a standard TREC collection. The results show that our framework markedly outperforms state-ofthe-art diversification approaches under a simulated best-case scenario. Moreover, we show that its effectiveness can be further improved by estimating the relative importance of each identified sub-query. Finally, we show that our framework can still outperform the simulated bestcase scenario of the state-of-the-art diversification approaches using subqueries automatically derived from the baseline document ranking itself. 1

Query-independent features (also called document priors), such as the number of incoming links to a document, its Page-Rank, or the type of its associated URL, have been successfully integrated into Web Information Retrieval systems in order to enhance the retrieval effectiveness. The combination of several document priors could further enhance the retrieval performance. However, most current combination of priors approaches are based on heuristics, and often ignore the possible dependence between the document priors. In this paper, we present a novel and robust method for conditionally combining document priors in a principled way. The approach adjusts the distribution of document priors for one source of evidence according to the distribution of document priors for other sources of evidence. We investigate the retrieval performance attainable by our combination of priors method, in comparison to the use of single priors and to a heuristic combination of document priors method, which assumes that document priors are independent. Furthermore, we investigate how sensitive the proposed method is to the training data. Using two standard Web test collections, including the large-scale.GOV2 test collection, we find that some of the document priors used in our experiments, have a considerably high correlation, suggesting that the dependency between documents priors should indeed be taken into account. Through extensive experiments on these two large-scale collections, we observe that our proposed conditional combination method is overall effective and robust. 1

Abstract. In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise- for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their “expertise need ” by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks. 1

Abstract We present a fully automatic and weighted dictionary to be used in topical opinion retrieval. We also define a simple topical opinion retrieval function that is free from parameters, so that the retrieval does not need any learning or tuning phase. 1

Search result diversification has gained momentum as a way to tackle ambiguous queries. An effective approach to this problem is to explicitly model the possible aspects underlying a query, in order to maximise the estimated relevance of the retrieved documents with respect to the different aspects. However, such aspects themselves may represent information needs with rather distinct intents (e.g., informational or navigational). Hence, a diverse ranking could benefit from applying intent-aware retrieval models when estimating the relevance of documents to different aspects. In this paper, we propose to diversify the results retrieved for a given query, by learning the appropriateness of different retrieval models for each of the aspects underlying this query. Thorough experiments within the evaluation framework provided by the diversity task of the TREC 2009 and 2010 Web tracks show that the proposed approach can significantly improve state-of-the-art diversification approaches.

Abstract. Document fields, such as the title or the headings of a document, offer a way to consider the structure of documents for retrieval. Most of the proposed approaches in the literature employ either a linear combination of scores assigned to different fields, or a linear combination of frequencies in the term frequency normalisation component. In the context of the Divergence From Randomness framework, we have a sound opportunity to integrate document fields in the probabilistic randomness model. This paper introduces novel probabilistic models for incorporating fields in the retrieval process using a multinomial randomness model and its information theoretic approximation. The evaluation results from experiments conducted with a standard TREC Web test collection show that the proposed models perform as well as a state-of-the-art field-based weighting model, while at the same time, they are theoretically founded and more extensible than current field-based models. 1

Abstract: Expert search is a task of growing importance in Enterprise settings. In a classical search setting, users normally require relevant documents to fulfil an information need. However, in Enterprise settings, users also have a need to identify the co-workers with relevant expertise to a topic area. An expert search engine assists users with their expertise need, by ranking candidate experts with respect to their predicted expertise about a topic of interest. This work presents a novel model for the expert search, based on a Bayesian belief network. We show how the proposed model can generate several different strategies for ranking candidates by their predicted expertise with respect to a query. The Bayesian belief network model for expert search proposed here is general, as it can be extended in the future to take into account various other types of evidence in the expert search task, such as the social aspect of expert search, where people work within groups and co-author publications. 1

Information Retrieval (IR) systems require input corpora to be indexed. The advent of terabyte-scale Web corpora has reinvigorated the need for efficient indexing. In this work, we investigate distributed indexing paradigms, in particular within the auspices of the MapReduce programming framework. In particular, we describe two indexing approaches based on the original MapReduce paper, and compare these with a standard distributed IR system, the MapReduce indexing strategy used by the Nutch IR platform, and a more advanced MapReduce indexing implementation that we propose. Experiments using the Hadoop MapReduce implementation and a large standard TREC corpus show our proposed MapReduce indexing implementation to be more efficient than those proposed in the original paper. 1.

This year the DCU-CLARITY-iAD team participated in the both the instance search and interactive known-item search tasks of TRECVid 2010. For our instance search submission, we used classifiers to search for candidate objects in each keyframe. This was achieved by a coarse-to-fine search based on a hierarchical representation of the regions in the keyframes. Our results proved inconclusive, but we believe the method warrants further investigation. The 2010 interactive search task at TRECVid represents a number of firsts for the community and for our team it represents the first time that many of our team has participated in TRECVid. Our approach this year was to develop a simple and intuitive system which we felt could be used by video information seeking specialists and complete novices alike. To this end we have developed our 2010 TRECVid KIS system on an Apple iPad, the iPad is a new tablet computer developed by Apple, it represents a lean-back, relaxed and easy to use computer, likewise our search engine was designed to be easy to use by all users. Our underlying search engine allows for the three commonly used video search methods: text search, concept search and image search. For our experiments we compared the performance of our system when used by standard users from our research group versus novices with no technical expertise. Our results show that the two groups gave identical performance in terms of mean elapsed time. 1