This paper presents a new user feedback mechanism based on Wikipedia concepts for interactive retrieval. In this mechanism, the system presents to the user a group of Wikipedia concepts, and the user can choose those relevant to refine his her query. To realize this mechanism, we propose methods to address two problems: 1) how to select a small number of possibly relevant Wikipedia concepts to show the user, and 2) how to re-rank retrieved documents given the user-identified Wikipedia concepts. Our methods are evaluated on three TREC data sets. The experiment results show that our methods can dramatically improve retrieval performances.
Wikipedia has been shown to be a useful resource for many intelligent tasks, including pseudo relevance feedback @cite_20 , query expansion @cite_16 , cross-language information retrieval @cite_22 @cite_12 , text classification @cite_17 , etc. @cite_20 propose a query-dependent method for selecting Wikipedia articles for pseudo relevance feedback. @cite_16 propose to use Wikipedia as an external corpus to expand difficult queries. In this paper, we explore the usage of Wikipedia in interactive retrieval and propose a new user feedback mechanism based on Wikipedia.
@cite_22 @cite_16 @cite_20 @cite_12 @cite_17
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Pseudo-relevance feedback (PRF) via query-expansion has been proven to be e®ective in many information retrieval (IR) tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for PRF. One problem with this approach is that one or more of the top retrieved documents may be non-relevant, which can introduce noise into the feedback process. Besides, existing methods generally do not take into account the significantly different types of queries that are often entered into an IR system. Intuitively, Wikipedia can be seen as a large, manually edited document collection which could be exploited to improve document retrieval effectiveness within PRF. It is not obvious how we might best utilize information from Wikipedia in PRF, and to date, the potential of Wikipedia for this task has been largely unexplored. In our work, we present a systematic exploration of the utilization of Wikipedia in PRF for query dependent expansion. Specifically, we classify TREC topics into three categories based on Wikipedia: 1) entity queries, 2) ambiguous queries, and 3) broader queries. We propose and study the effectiveness of three methods for expansion term selection, each modeling the Wikipedia based pseudo-relevance information from a different perspective. We incorporate the expansion terms into the original query and use language modeling IR to evaluate these methods. Experiments on four TREC test collections, including the large web collection GOV2, show that retrieval performance of each type of query can be improved. In addition, we demonstrate that the proposed method out-performs the baseline relevance model in terms of precision and robustness. Query expansion methods using pseudo-relevance feedback have been shown effective for microblog search because they can solve vocabulary mismatch problems often seen in searching short documents such as Twitter messages (tweets), which are limited to 140 characters. Pseudo-relevance feedback assumes that the top ranked documents in the initial search results are relevant and that they contain topic-related words appropriate for relevance feedback. However, those assumptions do not always hold in reality because the initial search results often contain many irrelevant documents. In such a case, only a few of the suggested expansion words may be useful with many others being useless or even harmful. To overcome the limitation of pseudo-relevance feedback for microblog search, we propose a novel query expansion method based on two-stage relevance feedback that models search interests by manual tweet selection and integration of lexical and temporal evidence into its relevance model. Our experiments using a corpus of microblog data (the Tweets2011 corpus) demonstrate that the proposed two-stage relevance feedback approaches considerably improve search result relevance over almost all topics. The online encyclopedia Wikipedia is a highly successful “open content” project, written and maintained completely by volunteers. Little is known, however, about the motivation of these volunteers. Results from an online survey among 106 contributors to the German Wikipedia project are presented. Both motives derived from social sciences (perceived benefits, identification with Wikipedia, etc.) as well as perceived task characteristics (autonomy, skill variety, etc.) were assessed as potential predictors of contributors' satisfaction and self-reported engagement. Satisfaction ratings were particularly determined by perceived benefits, identification with the Wikipedia community, and task characteristics. Engagement was particularly determined by high tolerance for opportunity costs and by task characteristics, the latter effect being partially mediated by intrinsic motivation. Relevant task characteristics for contributors' engagement and satisfaction were perceived autonomy, task significance, skill vari... Pseudo-relevance feedback has proven to be an effective strategy for improving retrieval accuracy in all retrieval models. However the performance of existing pseudo feedback methods is often affected significantly by some parameters, such as the number of feedback documents to use and the relative weight of original query terms; these parameters generally have to be set by trial-and-error without any guidance. In this paper, we present a more robust method for pseudo feedback based on statistical language models. Our main idea is to integrate the original query with feedback documents in a single probabilistic mixture model and regularize the estimation of the language model parameters in the model so that the information in the feedback documents can be gradually added to the original query. Unlike most existing feedback methods, our new method has no parameter to tune. Experiment results on two representative data sets show that the new method is significantly more robust than a state-of-the-art baseline language modeling approach for feedback with comparable or better retrieval accuracy.