Cluster-based retrieval using language models

Liu, Xiaoyong; Croft, W. Bruce

doi:10.1145/1008992.1009026

Cited by 341 publications

(388 citation statements)

References 25 publications

(41 reference statements)

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“…Finally, many recent improvements in core information retrieval have come from classification and clustering, e.g. viewing document retrieval as a text classification problem (Manning et al, 2008, chapters 11 & 12) or improving retrieval performance using clustering (Liu and Croft, 2004).…”

Section: Data Mining and Machine Learning For Irmentioning

confidence: 99%

Teaching IR: Curricular Considerations

Blank

Fuhr

Henrich

et al. 2011

Teaching and Learning in Information Retrieval

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Section: Data Mining and Machine Learning For Irmentioning

confidence: 99%

Teaching IR: Curricular Considerations

Blank

Fuhr

Henrich

et al. 2011

Teaching and Learning in Information Retrieval

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“…They use a language modeling framework in which their aspect-x algorithm smoothes documents based on the information from the clusters and the strength of the connection between each document and cluster. Liu and Croft (2004) evaluate both the direct retrieval of clusters and cluster-based smoothing. Their CBDM model is a mixture between a document model, a collection model, and the cluster each document belongs to, which is able to significantly outperform a standard query-likelihood baseline.…”

Section: Related Workmentioning

confidence: 99%

“…The other parameters are investigated in the range [1,10] with increments of 1. We determine the MAP scores on the same topics that we present results for, similar to Liu and Croft (2004), Metzler and Croft (2005), Mitra et al (1998), Lafferty et al (2001) and Zhai and Lafferty (2004). While computationally expensive (exponential in the number of parameters), it does provide us with an upper bound on the retrieval performance that one might achieve using the described models.…”

Section: Parameter Estimationmentioning

confidence: 99%

Conceptual language models for domain-specific retrieval

Meij¹,

Trieschnigg

Rijke³

et al. 2010

Information Processing & Management

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a b s t r a c tOver the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within the language modeling framework, which leverages these conceptual annotations to improve retrieval. By means of relevance feedback the original query is translated into a conceptual representation, which is subsequently used to update the query model.Extensive experimental work on five test collections in two domains shows that our approach gives significant improvements in terms of recall, initial precision and mean average precision with respect to a baseline without relevance feedback. On one test collection, it is also able to outperform a text-based pseudo-relevance feedback approach based on relevance models. On the other test collections it performs similarly to relevance models. Overall, conceptual language models have the added advantage of offering query and browsing suggestions in the form of conceptual annotations. In addition, the internal structure of the meta-language can be exploited to add related terms.Our contributions are threefold. First, an extensive study is conducted on how to effectively translate a textual query into a conceptual representation. Second, we propose a method for updating a textual query model using the concepts in conceptual representation. Finally, we provide an extensive analysis of when and how this conceptual feedback improves retrieval.

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“…There are several studies that show the benefit of document-side expansion by extracting features from similar documents based on the language models (e.g. [17,20]). However, the particularity of the ad retrieval and the relationship between the landing page and the ads makes our problem significantly different than the setting explored there.…”

Section: Sponsored Search and Content Matchmentioning

confidence: 99%

Using landing pages for sponsored search ad selection

Choi

Fontoura

Gabrilovich

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

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We explore the use of the landing page content in sponsored search ad selection. Specifically, we compare the use of the ad's intrinsic content to augmenting the ad with the whole, or parts, of the landing page. We explore two types of extractive summarization techniques to select useful regions from the landing pages: out-of-context and in-context methods. Out-of-context methods select salient regions from the landing page by analyzing the content alone, without taking into account the ad associated with the landing page. In-context methods use the ad context (including its title, creative, and bid phrases) to help identify regions of the landing page that should be used by the ad selection engine. In addition, we introduce a simple yet effective unsupervised algorithm to enrich the ad context to further improve the ad selection. Experimental evaluation confirms that the use of landing pages can significantly improve the quality of ad selection. We also find that our extractive summarization techniques reduce the size of landing pages substantially, while retaining or even improving the performance of ad retrieval over the method that utilize the entire landing page.

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Cluster-based retrieval using language models

Cited by 341 publications

References 25 publications

Teaching IR: Curricular Considerations

Teaching IR: Curricular Considerations

Conceptual language models for domain-specific retrieval

Using landing pages for sponsored search ad selection

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