Cluster-based retrieval using language models

By Xiaoyong Liu, W. Bruce Croft. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004

516 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/f5d75477dbf18f17f5bcf66c862b8c4830970c7c

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Citations in the corpus

Entity based Q&A Retrieval (2012, methodology): “Others (Liu and Croft, 2004) perform semantic smoothing by means of clustering.”
Estimation of statistical translation models based on mutual information for ad hoc information retrieval (2010, background): “For example, cluster or topic-model based approaches have been studied in [16, 36].”
Neural information retrieval: at the end of the early years (2017, methodology): “Besides the QLMs, a relevance model (Lavrenko and Croft 2001), which computes a feedback query language model using embedding similarities in addition to term matching, is introduced.”
Video search reranking via information bottleneck principle (2006, methodology): “The cluster denoising process has been shown effective in text search [13].”
Retrieval from software libraries for bug localization: a comparative study of generic and composite text models (2011, methodology): “Finally, we review the Cluster Based Document Model (CBDM), another relatively popular approach for text modeling [16].”
Enhancing Expert Finding Using Organizational Hierarchies (2009, methodology): “While most smoothing methods utilize the global collection information with a simple interpolation approach [10],[18],[22],[29], other studies [6],[15],[16],[17],[25],[28] have shown that local corpus structures can be used to improve retrieval performance.”
LDA-based document models for ad-hoc retrieval (2006, methodology): “We present the new retrieval model based on LDA in Section 3, and compare the complexity of LDA with the clustering algorithms used in Liu and Croft (2004) in Section 4.”
A survey of Web clustering engines (2009, background): “In this case the system usually returns the items contained in one or more optimal clusters [Tombros et al. 2002; Liu and Croft 2004].”
Analysis of the Paragraph Vector Model for Information Retrieval (2016, methodology): “As observed by previous studies [24, 15, 1], topic level estimation is beneficial for language modeling approach.”
Book Review: Statistical Language Models for Information Retrieval by ChengXiang Zhai (2008, background): “© ChengXiang Zhai, 2006 62 Cluster-based Smoothing/Scoring • Cluster-based smoothing: Smooth a document LM with a cluster of similar documents [Liu & Croft 04]: improves over the basic LM, but insignificantly • Document expansion smoothing: Smooth a document LM with the neighboring documents (essent”
Improving retrieval of short texts through document expansion (2012, background): “For instance, Liu and Croft applied clusters during document language model smoothing [18].”
Discovering key concepts in verbose queries (2008, unknown): “A common way to estimate a joint conditional probability is using a linear interpolation of the individual conditional probabilities [20, 18, 30].”