Search (6 results, page 1 of 1)

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Abstract

Academic search concerns the retrieval and profiling of information objects in the domain of academic research. In this paper we reveal important observations of academic search queries, and provide an algorithmic solution to address a type of failure during search sessions: null queries. We start by providing a general characterization of academic search queries, by analyzing a large-scale transaction log of a leading academic search engine. Unlike previous small-scale analyses of academic search queries, we find important differences with query characteristics known from web search. E.g., in academic search there is a substantially bigger proportion of entity queries, and a heavier tail in query length distribution. We then focus on search failures and, in particular, on null queries that lead to an empty search engine result page, on null sessions that contain such null queries, and on users who are prone to issue null queries. In academic search approximately 1 in 10 queries is a null query, and 25% of the sessions contain a null query. They appear in different types of search sessions, and prevent users from achieving their search goal. To address the high rate of null queries in academic search, we consider the task of providing query suggestions. Specifically we focus on a highly frequent query type: non-boolean informational queries. To this end we need to overcome query sparsity and make effective use of session information. We find that using entities helps to surface more relevant query suggestions in the face of query sparsity. We also find that query suggestions should be conditioned on the type of session in which they are offered to be more effective. After casting the session classification problem as a multi-label classification problem, we generate session-conditional query suggestions based on predicted session type. We find that this session-conditional method leads to significant improvements over a generic query suggestion method. Personalization yields very little further improvements over session-conditional query suggestions.

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Abstract

In this paper, we compare a well-known semantic spacemodel, Latent Semantic Analysis (LSA) with another model, Hyperspace Analogue to Language (HAL) which is widely used in different area, especially in automatic query refinement. We conduct this comparative analysis to prove our hypothesis that with respect to ability of extracting the lexical information from a corpus of text, LSA is quite similar to HAL. We regard HAL and LSA as black boxes. Through a Pearson's correlation analysis to the outputs of these two black boxes, we conclude that LSA highly co-relates with HAL and thus there is a justification that LSA and HAL can potentially play a similar role in the area of facilitating automatic query refinement. This paper evaluates LSA in a new application area and contributes an effective way to compare different semantic space models.

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Abstract

Purpose - The purpose of this paper is to claim that it is useful to assess the web connectivity of research groups, describe hyperlink-based techniques to achieve this and present brief details of European life sciences research groups as a case study. Design/methodology/approach - A commercial search engine was harnessed to deliver hyperlink data via its automatic query submission interface. A special purpose link analysis tool, LexiURL, then summarised and graphed the link data in appropriate ways. Findings - Webometrics can provide a wide range of descriptive information about the international connectivity of research groups. Research limitations/implications - Only one field was analysed, data was taken from only one search engine, and the results were not validated. Practical implications - Web connectivity seems to be particularly important for attracting overseas job applicants and to promote research achievements and capabilities, and hence we contend that it can be useful for national and international governments to use webometrics to ensure that the web is being used effectively by research groups. Originality/value - This is the first paper to make a case for the value of using a range of webometric techniques to evaluate the web presences of research groups within a field, and possibly the first "applied" webometrics study produced for an external contract.

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Abstract

In this paper, a new robust relevance model is proposed that can be applied to both pseudo and true relevance feedback in the language-modeling framework for document retrieval. There are at least three main differences between our new relevance model and other relevance models. The proposed model brings back the original query into the relevance model by treating it as a short, special document, in addition to a number of top-ranked documents returned from the first round retrieval for pseudo feedback, or a number of relevant documents for true relevance feedback. Second, instead of using a uniform prior as in the original relevance model proposed by Lavrenko and Croft, documents are assigned with different priors according to their lengths (in terms) and ranks in the first round retrieval. Third, the probability of a term in the relevance model is further adjusted by its probability in a background language model. In both pseudo and true relevance cases, we have compared the performance of our model to that of the two baselines: the original relevance model and a linear combination model. Our experimental results show that the proposed new model outperforms both of the two baselines in terms of mean average precision.

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Date

28. 1.2006 19:21:22

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Date

20. 1.2015 18:30:22