Towards Query Level Resource Weighting for Diversified Query Expansion

Bouchoucha, Arbi; Liu, Xiaohua; Nie, Jian‐Yun

doi:10.1007/978-3-319-16354-3_1

Cited by 2 publications

(2 citation statements)

References 17 publications

(49 reference statements)

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“…Since all previous efforts on DQE use evaluation measures that are based on expensive human-inputs in the form of releveance judgements (e.g., [4,27]), we now devise an intuitive and automated metric to evaluate the diversity of DQE results by mapping them to the entity space where external entity relatedness measures can be exploited. In other words, this evaluation measure quantifies the diversity of the entities that the DQE output maps to.…”

Section: Automated Diversity Evaluationmentioning

confidence: 99%

Leveraging semantic resources in diversified query expansion

et al. 2017

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A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. In this paper, we consider the usage of semantic resources and tools to arrive at improved methods for diversified query expansion. In particular, we develop two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively. Both the approaches operate on a common three-phase framework; that of first taking a set of informative terms from the search results of the initial query, then building a graph, following by using a diversity-conscious node ranking to prioritize candidate terms for diversified query expansion. Our methods differ in the second phase, with the first method Select-Link-Rank (SLR) linking terms with Wikipedia entities to accomplish graph construction; on the other hand, our second method, SelectEmbed-Rank (SER), constructs the graph using similarities between distributional word embeddings. Through an empirical analysis and user study, we show that SLR ourperforms state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversified query expansion. Our empirical analysis also illustrates that SER outperforms the baselines convincingly, asserting that it is the best available method for those cases where SLR is not applicable; these include narrow-focus search systems where a relevant knowledge base is unavailable. Our SLR method is also seen to outperform a state-of-the-art method in the task of diversified entity ranking.

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Section: Automated Diversity Evaluationmentioning

confidence: 99%

Leveraging semantic resources in diversified query expansion

et al. 2017

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“…Since all previous efforts on DQE use evaluation measures that are based on expensive human-inputs in the form of releveance judgements (e.g., [21,4]), we now devise an intuitive and automated metric to evaluate the diversity of DQE results by mapping them to the entity space where external entity relatedness measures can be exploited. Consider the top-k query expansions as E; we start by finding the set of entity nodes associated with those expansions, (a) Jaccard Similarity (b) Dexter Similarity Fig.…”

Section: Automated Diversity Evaluationmentioning

confidence: 99%

Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia

Krishnan

Deepak

Ranu

et al. 2016

Web Information Systems Engineering – WISE 2016

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Abstract. A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. We propose a method, Select-Link-Rank, that exploits semantic information from Wikipedia to generate diversified query expansions. SLR does collective processing of terms and Wikipedia entities in an integrated framework, simultaneously diversifying query expansions and entity recommendations. SLR starts with selecting informative terms from search results of the initial query, links them to Wikipedia entities, performs a diversity-conscious entity scoring and transfers such scoring to the term space to arrive at query expansion suggestions. Through an extensive empirical analysis and user study, we show that our method outperforms the state-of-the-art diversified query expansion and diversified entity recommendation techniques.

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Towards Query Level Resource Weighting for Diversified Query Expansion

Cited by 2 publications

References 17 publications

Leveraging semantic resources in diversified query expansion

Leveraging semantic resources in diversified query expansion

Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia

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