A simple term frequency transformation model for effective pseudo relevance feedback

Zheng, Ye; Huang, Jimmy Xiangji

doi:10.1145/2600428.2609636

Cited by 18 publications

(20 citation statements)

References 44 publications

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“…The details of these collections are shown in Table 1. In all the experiments, we only used the title field of the TREC queries for retrieval, because it is closer to the actual queries used in the real web search applications and relevance feedback is expected to be the most useful for short queries [19].…”

Section: Methodsmentioning

confidence: 99%

A Study of Collection-Based Features for Adapting the Balance Parameter in Pseudo Relevance Feedback

Meng

Zhang

Song

et al. 2015

Information Retrieval Technology

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Abstract. Pseudo-relevance feedback (PRF) is an effective technique to improve the ad-hoc retrieval performance. For PRF methods, how to optimize the balance parameter between the original query model and feedback model is an important but difficult problem. Traditionally, the balance parameter is often manually tested and set to a fixed value across collections and queries. However, due to the difference among collections and individual queries, this parameter should be tuned differently. Recent research has studied various query based and feedback documents based features to predict the optimal balance parameter for each query on a specific collection, through a learning approach based on logistic regression. In this paper, we hypothesize that characteristics of collections are also important for the prediction. We propose and systematically investigate a series of collection-based features for queries, feedback documents and candidate expansion terms. The experiments show that our method is competitive in improving retrieval performance and particularly for cross-collection prediction, in comparison with the state-of-the-art approaches.

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Section: Methodsmentioning

confidence: 99%

A Study of Collection-Based Features for Adapting the Balance Parameter in Pseudo Relevance Feedback

Meng

Zhang

Song

et al. 2015

Information Retrieval Technology

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“…A simple term frequency transformation model [46] was another stream of work which is based on traditional modelling of QE. This work proposed a simple and heuristic model, in which three term frequency transformation techniques were integrated to capture the saliency of a candidate term associated with the original query terms in the feedback documents.…”

Section: Related Work On Qementioning

confidence: 99%

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Khennak

Drias

2017

Appl Intell

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Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

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“…Many models with relevance feedback have been proposed [2,7,11,[15][16][17], among which the relevance model (RM) [2] and the mixture model feedback (MMF) [7] are two basic models on which many other models are built.…”

Section: Rm and Mmfmentioning

confidence: 99%

Generalized Analysis of a Distribution Separation Method

Zhang

Qian

Hou

et al. 2016

Entropy

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Separating two probability distributions from a mixture model that is made up of the combinations of the two is essential to a wide range of applications. For example, in information retrieval (IR), there often exists a mixture distribution consisting of a relevance distribution that we need to estimate and an irrelevance distribution that we hope to get rid of. Recently, a distribution separation method (DSM) was proposed to approximate the relevance distribution, by separating a seed irrelevance distribution from the mixture distribution. It was successfully applied to an IR task, namely pseudo-relevance feedback (PRF), where the query expansion model is often a mixture term distribution. Although initially developed in the context of IR, DSM is indeed a general mathematical formulation for probability distribution separation. Thus, it is important to further generalize its basic analysis and to explore its connections to other related methods. In this article, we first extend DSM's theoretical analysis, which was originally based on the Pearson correlation coefficient, to entropy-related measures, including the KL-divergence (Kullback-Leibler divergence), the symmetrized KL-divergence and the JS-divergence (Jensen-Shannon divergence). Second, we investigate the distribution separation idea in a well-known method, namely the mixture model feedback (MMF) approach. We prove that MMF also complies with the linear combination assumption, and then, DSM's linear separation algorithm can largely simplify the EM algorithm in MMF. These theoretical analyses, as well as further empirical evaluation results demonstrate the advantages of our DSM approach.

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A simple term frequency transformation model for effective pseudo relevance feedback

Cited by 18 publications

References 44 publications

A Study of Collection-Based Features for Adapting the Balance Parameter in Pseudo Relevance Feedback

A Study of Collection-Based Features for Adapting the Balance Parameter in Pseudo Relevance Feedback

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Generalized Analysis of a Distribution Separation Method

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