Ranking document clusters using markov random fields

Raiber, Fiana; Kurland, Oren

doi:10.1145/2484028.2484042

Cited by 36 publications

(43 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, the significant improvements observed on the Robust-04 and GOV2 collections, are much lower on the Clueweb-09-Cat-B collection. The relatively low performance improvements with dependency models using the current Clueweb-09 queries has also been observed at TREC and in recent publications [4,23]. As more queries are developed for this corpus, we plan to study this issue further.…”

Section: Bi-term Dependency Model Comparisonmentioning

confidence: 67%

A Comparison of Retrieval Models using Term Dependencies

Huston

Croft

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

A number of retrieval models incorporating term dependencies have recently been introduced. Most of these modify existing "bag-ofwords" retrieval models by including features based on the proximity of pairs of terms (or bi-terms). Although these term dependency models have been shown to be significantly more effective than the bag-of-words models, there have been no previous systematic comparisons between the different approaches that have been proposed. In this paper, we compare the effectiveness of recent bi-term dependency models over a range of TREC collections, for both short (title) and long (description) queries. To ensure the reproducibility of our study, all experiments are performed on widely available TREC collections, and all tuned retrieval model parameters are made public. These comparisons show that the weighted sequential dependence model is at least as effective as, and often significantly better than, any other model across this range of collections and queries. We observe that dependency features are much more valuable in improving the performance of longer queries than for shorter queries. We then examine the effectiveness of dependence models that incorporate proximity features involving more than two terms. The results show that these features can improve effectiveness, but not consistently, over the available data sets.

show abstract

Section: Bi-term Dependency Model Comparisonmentioning

confidence: 67%

A Comparison of Retrieval Models using Term Dependencies

Huston

Croft

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

show abstract

“…Recall that k is also the number of nearest neighbors in the NN cluster hypothesis test. Using such small overlapping clusters was shown to be highly effective, with respect to other clustering schemes, for cluster-based retrieval [4,11,7,14,15].…”

Section: Methodsmentioning

confidence: 99%

“…Document clusters can be created either in a query dependent manner, i.e., from the list of documents most highly ranked in response to a query [21] or in a query independent fashion from all the documents in a collection [5,10]. In this paper we study the correlation between cluster hypothesis tests and the effectiveness of retrieval methods that utilize query dependent clusters [6,7,15]. The reason is threefold.…”

Section: Related Workmentioning

confidence: 99%

“…We also use the recently proposed state-of-the-art ClustMRF cluster ranking method [15]. ClustMRF uses Markov Random Fields which enable to integrate various types of cluster-relevance evidence.…”

Section: Cluster Hypothesis Tests and Cluster-based Retrievalmentioning

confidence: 99%

“…Various tests were devised for estimating the extent to which the hypothesis holds [5,20,3,17]. Furthermore, inspired by the hypothesis, document retrieval methods that utilize document clusters were proposed (e.g., [10,11,6,7,15]). …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The correlation between cluster hypothesis tests and the effectiveness of cluster-based retrieval

Raiber

Kurland

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

We present a study of the correlation between the extent to which the cluster hypothesis holds, as measured by various tests, and the relative effectiveness of cluster-based retrieval with respect to document-based retrieval. We show that the correlation can be affected by several factors, such as the size of the result list of the most highly ranked documents that is analyzed. We further show that some cluster hypothesis tests are often negatively correlated with one another. Moreover, in several settings, some of the tests are also negatively correlated with the relative effectiveness of cluster-based retrieval.

show abstract

Cocluster hypothesis and ranking consistency for relevance ranking in web search

Jiang

Cheng

2019

Asso for Info Science & Tech

View full text Add to dashboard Cite

Conventional approaches to relevance ranking typically optimize ranking models by each query separately. The traditional cluster hypothesis also does not consider the dependency between related queries. The goal of this paper is to leverage similar search intents to perform ranking consistency so that the search performance can be improved accordingly. Different from the previous supervised approach, which learns relevance by click‐through data, we propose a novel cocluster hypothesis to bridge the gap between relevance ranking and ranking consistency. A nearest‐neighbors test is also designed to measure the extent to which the cocluster hypothesis holds. Based on the hypothesis, we further propose a two‐stage unsupervised approach, in which two ranking heuristics and a cost function are developed to optimize the combination of consistency and uniqueness (or inconsistency). Extensive experiments have been conducted on a real and large‐scale search engine log. The experimental results not only verify the applicability of the proposed cocluster hypothesis but also show that our approach is effective in boosting the retrieval performance of the commercial search engine and reaches a comparable performance to the supervised approach.

show abstract

Ranking document clusters using markov random fields

Cited by 36 publications

References 36 publications

A Comparison of Retrieval Models using Term Dependencies

A Comparison of Retrieval Models using Term Dependencies

The correlation between cluster hypothesis tests and the effectiveness of cluster-based retrieval

Cocluster hypothesis and ranking consistency for relevance ranking in web search

Contact Info

Product

Resources

About