Finding a good query‐related topic for boosting pseudo‐relevance feedback

Zheng, Yi; Huang, Jimmy Xiangji; Lin, Hongfei

doi:10.1002/asi.21501

Cited by 36 publications

(11 citation statements)

References 37 publications

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“…In this paper, we extend our previous studies around the Latent Concept Modeling framework (Deveaud et al, 2013b), which mainly consists at applying topic modeling algorithms such as LDA to a small set of pseudo-relevant feedback documents (Deveaud et al, 2013a;Ye et al, 2011). While we recall the main principles of our method in the next section, we perform a thorough evaluation of the estimated parameters and of the retrieval effectiveness.…”

Section: Related Workmentioning

confidence: 92%

Accurate and effective latent concept modeling for ad hoc information retrieval

Deveaud¹,

SanJuan²,

Bellot³

2014

Document numérique

413

253

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ABSTRACT. A keyword query is the representation of the information need of a user, and is the result of a complex cognitive process which often results in under-specification. We propose an unsupervised method namely Latent Concept Modeling (LCM) for mining and modeling latent search concepts in order to recreate the conceptual view of the original information need. We use Latent Dirichlet Allocation (LDA) to exhibit highly-specific query-related topics from pseudo-relevant feedback documents. We define these topics as the latent concepts of the user query. We perform a thorough evaluation of our approach over two large ad-hoc TREC collections. Our findings reveal that the proposed method accurately models latent concepts, while being very effective in a query expansion retrieval setting.RÉSUMÉ. Une requête est la représentation du besoin d'information d'un utilisateur, et est le résultat d'un processus cognitif complexe qui mène souvent à un mauvais choix de mots-clés. Nous proposons une méthode non supervisée pour la modélisation de concepts implicites d'une requête, dans le but de recréer la représentation conceptuelle du besoin d'information initial. Nous utilisons l'allocation de Dirichlet latente (LDA) pour détecter les concepts implicites de la requête en utilisant des documents pseudo-pertinents. Nous évaluons cette méthode en profondeur en utilisant deux collections de test de TREC. Nous trouvons notamment que notre approche permet de modéliser précisément les concepts implicites de la requête, tout en obtenant de bonnes performances dans le cadre d'une recherche de documents.

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Section: Related Workmentioning

confidence: 92%

Accurate and effective latent concept modeling for ad hoc information retrieval

Deveaud¹,

SanJuan²,

Bellot³

2014

Document numérique

413

253

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“…Since the top‐ k retrieved images are assumed and used as the positive feedback set, the number of the feedback set is crucial to the PRF performance (Ye, Huang, & Lin, ). However, many related studies have only used a fixed set of pseudopositive/‐negative images for PRF, such as Deselaers, Paredes et al.…”

Section: Important Factors Of Prfmentioning

confidence: 99%

Factors affecting rocchio‐based pseudorelevance feedback in image retrieval

Tsai

Chen

2014

Asso for Info Science & Tech

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Pseudorelevance feedback (PRF) was proposed to solve the limitation of relevance feedback (RF), which is based on the user-in-the-loop process. In PRF, the top-k retrieved images are regarded as PRF. Although the PRF set contains noise, PRF has proven effective for automatically improving the overall retrieval result. To implement PRF, the Rocchio algorithm has been considered as a reasonable and well-established baseline. However, the performance of Rocchio-based PRF is subject to various representation choices (or factors). In this article, we examine these factors that affect the performance of Rocchio-based PRF, including image-feature representation, the number of top-ranked images, the weighting parameters of Rocchio, and similarity measure. We offer practical insights on how to optimize the performance of Rocchio-based PRF by choosing appropriate representation choices. Our extensive experiments on NUS-WIDE-LITE and Caltech 101 + Corel 5000 data sets show that the optimal feature representation is color moment + wavelet texture in terms of retrieval efficiency and effectiveness. Other representation choices are that using top-20 ranked images as pseudopositive and pseudonegative feedback sets with the equal weight (i.e., 0.5) by the correlation and cosine distance functions can produce the optimal retrieval result.

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“…It assumes that topranked documents in the first-pass retrieval are relevant, and then used as feedback documents in order to refine the representation of original queries by adding potentially related terms. Although PRF has been shown to be effective in improving IR performance [4,6,9,13,23,26,28,30,36,37,40,42] in a number of IR tasks, traditional PRF can also fail in some cases. For example, when some of the feedback documents have several incoherent topics, terms in the irrelevant contents are likely to misguide the feedback models by importing noisy terms into the queries.…”

Section: Introductionmentioning

confidence: 98%

Proximity-based rocchio's model for pseudo relevance

Miao

Huang

Zheng

2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Rocchio's relevance feedback model is a classic query expansion method and it has been shown to be effective in boosting information retrieval performance. The selection of expansion terms in this method, however, does not take into account the relationship between the candidate terms and the query terms (e.g., term proximity). Intuitively, the proximity between candidate expansion terms and query terms can be exploited in the process of query expansion, since terms closer to query terms are more likely to be related to the query topic.In this paper, we study how to incorporate proximity information into the Rocchio's model, and propose a proximitybased Rocchio's model, called PRoc, with three variants. In our PRoc models, a new concept (proximity-based term frequency, ptf ) is introduced to model the proximity information in the pseudo relevant documents, which is then used in three kinds of proximity measures. Experimental results on TREC collections show that our proposed PRoc models are effective and generally superior to the state-of-the-art relevance feedback models with optimal parameters. A direct comparison with positional relevance model (PRM) on the GOV2 collection also indicates our proposed model is at least competitive to the most recent progress.

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Finding a good query‐related topic for boosting pseudo‐relevance feedback

Cited by 36 publications

References 37 publications

Accurate and effective latent concept modeling for ad hoc information retrieval

Accurate and effective latent concept modeling for ad hoc information retrieval

Factors affecting rocchio‐based pseudorelevance feedback in image retrieval

Proximity-based rocchio's model for pseudo relevance

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