Towards a Better Understanding of the Relationship between Probabilistic Models in IR

Aly, Robin; Demeester, Thomas

doi:10.1007/978-3-642-23318-0_16

Cited by 3 publications

(4 citation statements)

References 17 publications

(19 reference statements)

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“…Therefore, we believe a promising research direction is to define new ranking principles that language models do follow. An alternative direction is to investigate the similarity of language model ranking functions with score functions from models that do follow an existing ranking principle, akin to but more general than our approach in Aly and Demeester [2011] (Sec. 5) or the one by Roelleke and Wang [2006].…”

Section: Discussionmentioning

confidence: 99%

“…This approach is complementary to our paper: we investigate the connection between probabilistic models, whereas Roelleke and Wang investigate connection between ranking functions that are derived from these models. Note that although we focus in this paper on the probabilistic models of PR models and ranking principles, we showed in Aly and Demeester [2011] an alternative connection between the mentioned ranking functions compared to the connection proposed by Roelleke and Wang.…”

Section: Related Workmentioning

confidence: 91%

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Probabilistic models in IR and their relationships

2013

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A solid research path towards new information retrieval (IR) models is to further develop the theory behind existing models. A profound understanding of these models is therefore essential. In this paper, we revisit Probability Ranking Principle (PRP)-based models, Probability of Relevance (PR) models, and language models, finding conceptual differences in their definition and interrelationships. The probabilistic model of the PRP has not been explicitly defined previously, but doing so leads to the formulation of two actual principles with different objectives. First, the belief probability ranking principle (BPRP), which considers uncertain relevance between known documents and the current query, and second, the popularity probability ranking principle (PPRP), which considers the probability of relevance of documents among multiple queries with the same features. Our analysis shows how some of the discussed PR models implement the BPRP or the PPRP while others do not. However, for some models the parameter estimation is challenging. Finally, language models are often presented as related to PR models. However, we find that language models differ from PR models in every aspect of a probabilistic model and the effectiveness of language models cannot be explained by the PRP.⋆ This paper is an extended version of Aly and Demeester [2011], with the following additional contributions: (1) the finding that there are actually two distinct PRPs, (2) the investigation of three additional models from the unified framework of PR models, and (3) a more elaborate discussion of related work. The final publication is available at

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 91%

Probabilistic models in IR and their relationships

2013

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“…• PRF, TF-IDF and LM: Recently, [2], "Towards a better understanding of the relationship between probabilistic models in IR", investigated some controversial aspects regarding the relationship between PRF (probabilistic odds), TF-IDF and LM.…”

Section: Ir Models: Relationships (90 Mins)mentioning

confidence: 99%

“…In summary, the tutorial is structured as follows: [20,11] IDF and BIR [14,6] Event spaces [16,18,10] Model axioms/constraints [7] LM and PRF/BM25: related? [9,2] LM and TF-IDF: siblings! [19] Thomas Roelleke is a senior lecturer at Queen Mary, University of London.…”

Section: Ir Models: Relationships (90 Mins)mentioning

confidence: 99%

IR models

Roelleke

2012

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

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In IR research it is essential to know IR models. Research over the past years has consolidated the foundations of IR models. Moreover, relationships have been reported that help to use and position IR models. Knowing about the foundations and relationships of IR models can significantly improve building information management systems. The first part of this tutorial presents an in-depth consolidation of the foundations of the main IR models (TF-IDF, BM25, LM). Particular attention will be given to notation and probabilistic roots. The second part crystallises the relationships between models. Does LM embody IDF? How "heuristic" is TF-IDF? What are the probabilistic roots? How are LM and the probability of relevance related? What are the components shared by the main IR models? After the tutorial, attendees will be familiar with a consolidated view on IR models. The tutorial will be illustrative and interactive, providing opportunities to exchange controversial issues and research challenges.

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Information Retrieval Models: Foundations and Relationships

Roelleke

2013

Synthesis Lectures on Information Concepts, Retrieval, and Serv

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Towards a Better Understanding of the Relationship between Probabilistic Models in IR

Cited by 3 publications

References 17 publications

Probabilistic models in IR and their relationships

Probabilistic models in IR and their relationships

IR models

Information Retrieval Models: Foundations and Relationships

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