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Probabilistic models of information retrieval based on measuring the divergence from randomness

Gianni Amati, Cornelis Joost Van Rijsbergen
2002 ACM Transactions on Information Systems  
We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process.  ...  We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach.  ...  The probabilistic models of randomness are based on the term-independence assumption.  ... 
doi:10.1145/582415.582416 fatcat:gpxakbh5jreq7mhzscasvn4qnm

Ad-hoc Retrieval on FIRE Data Set with TF-IDF and Probabilistic Models

Chandra ShekharJangid, Santosh K Vishwakarma, Kamaljit I Lakhtaria
2014 International Journal of Computer Applications  
Information Retrieval is finding documents of unstructured nature which should satisfy user's information needs. There exist various models for weighting terms of corpus documents and query terms.  ...  This work is carried out to analyze and evaluate the retrieval effectiveness of various IR models while using the new data set of FIRE 2011.  ...  We are using many version of different model such as Probabilistic models, TF-IDF weighting model and Divergence from randomness.  ... 
doi:10.5120/16435-6136 fatcat:h3zd2sn6erhhxhsidsokzfjao4

Information Retrieval and Graph Analysis Approaches for Book Recommendation

Chahinez Benkoussas, Patrice Bellot
2015 The Scientific World Journal  
We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination.  ...  A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query.  ...  Acknowledgment This work was supported by the French program "Investissements d'Avenir-Développement de l'Economie Numérique" under Project Inter-Textes no. O14751-408983.  ... 
doi:10.1155/2015/926418 pmid:26504899 pmcid:PMC4609525 fatcat:sm2cvhatorhtthcxnpiyhs6uam

The Impact of Linked Documents and Graph Analysis on Information Retrieval Methods for Book Recommendation

Chahinez Benkoussas, Patrice Bellot, Anais Ollagnier
2015 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)  
We used different theoretical retrieval models: probabilistic as InL2 (Divergence From Randomness model) and language models and tested their interpolated combination.  ...  A new combination of multiple Information Retrieval approaches are proposed for book recommendation based on complex users' queries.  ...  Divergence From Randomness (DFR) is one of several probabilistic models that we have used in our work.  ... 
doi:10.1109/wi-iat.2015.200 dblp:conf/webi/BenkoussasBO15 fatcat:2id5cguiovdo3imyznwf6baxn4

A Probabilistic Model for Information Retrieval Based on Maximum Value Distribution

Jiaul H. Paik
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
The main goal of a retrieval model is to measure the degree of relevance of a document with respect to the given query.  ...  Probabilistic models are widely used to measure the likelihood of relevance of a document by combining within document term frequency and term specificity in a formal way.  ...  One of the widely used non-parametric probabilistic model is divergence from randomness (DFR) [1] based approaches, where the term weight is computed by measuring the divergence between a term distribution  ... 
doi:10.1145/2766462.2767762 dblp:conf/sigir/Paik15 fatcat:z6bpb2pfabdbhd2ugo5oirk4ju

Combining Probabilistic and Translation-Based Models for Information Retrieval Based on Word Sense Annotations [chapter]

Elisabeth Wolf, Delphine Bernhard, Iryna Gurevych
2010 Lecture Notes in Computer Science  
On the other hand, we aimed at combining an often used probabilistic model, namely the Divergence From Randomness BM25 model (DFR BM25), with a monolingual translation-based model.  ...  On the one hand, we intended to increase the precision of WSD by a heuristic-based combination of the annotations of the two WSD systems.  ...  This framework provides state-of-the-art retrieval and query expansion models, such as the commonly used Divergence From Randomness (DFR) BM25 probabilistic model.  ... 
doi:10.1007/978-3-642-15754-7_14 fatcat:fklt42wakbfsxhmtqjv6oworuu

A Topic-Based Measure of Resource Description Quality for Distributed Information Retrieval [chapter]

Mark Baillie, Mark J. Carman, Fabio Crestani
2009 Lecture Notes in Computer Science  
Current measures for assessing sample quality are too coarse grain to be informative. This paper outlines a measure of finer granularity based on probabilistic topic models of text.  ...  Topics are both modelled from the collection and inferred in the sample using latent Dirichlet allocation.  ...  In the following sections we outline a new approach for evaluating resource description quality based on probabilistic topical modelling.  ... 
doi:10.1007/978-3-642-00958-7_43 fatcat:xz2fxclo4bgsdc2dt7ia2xz7zy

On the modelling of ranking algorithms in probabilistic datalog

Thomas Roelleke, Marco Bonzanini, Miguel Martinez-Alvarez
2013 Proceedings of the 7th International Workshop on Ranking in Databases - DBRank '13  
Though the ranking algorithms have probabilistic roots, the ranking score often is not probabilistic, leading to unsafe programs from a probabilistic point of view.  ...  In this paper, we describe the evolution of probabilistic Datalog to provide concepts required for modelling ranking algorithms.  ...  We focus on TF-IDF, BM25, language modelling (LM), and divergence-from-randomness (DFR). The remainder of this paper is structured as follows.  ... 
doi:10.1145/2524828.2524832 dblp:conf/vldb/RoellekeBM13 fatcat:li56czzxsbcmna4kvbw24khvru

IR Models

Thomas Roelleke
2013 Proceedings of the 2013 Conference on the Theory of Information Retrieval - ICTIR '13  
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.  ...  (Divergence from Randomness), and LM.  ... 
doi:10.1145/2499178.2499203 dblp:conf/ictir/Roelleke13 fatcat:5d5vdwyjznegzm4qtler76gcje

IR models

Thomas Roelleke
2012 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12  
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.  ...  (Divergence from Randomness), and LM.  ... 
doi:10.1145/2348283.2348535 dblp:conf/sigir/Roelleke12 fatcat:tdj2kn37tjcdpnduv3u66s7owy

Probabilistic Programming: A True Verification Challenge [chapter]

Joost-Pieter Katoen
2015 Lecture Notes in Computer Science  
In addition, probabilistic programs are an active research topic in quantitative information flow. Quantum programs are inherently probabilistic due to the random outcomes of quantum measurements.  ...  Starting from a profound understanding from the intricate semantics of probabilistic programs (including features such as observations, possibly diverging loops, continuous variables, non-determinism,  ...  This work is funded by the EU FP7-projects SENSATION and MEALS, and the Excellence Initiative of the German federal and state government.  ... 
doi:10.1007/978-3-319-24953-7_1 fatcat:i7bwx2oaufguhpzbedvqoap26m

Best and Fairest: An Empirical Analysis of Retrieval System Bias [chapter]

Colin Wilkie, Leif Azzopardi
2014 Lecture Notes in Computer Science  
This is largely motivated by the recent proposal of a new suite of retrieval models based on the Divergence From Independence (DFI) framework.  ...  In this paper, we explore the bias of term weighting schemes used by retrieval models.  ...  Summary and Conclusions In this paper, we have measured the retrieval bias of a spectrum of retrieval model/weightings to determine which model is the fairest.  ... 
doi:10.1007/978-3-319-06028-6_2 fatcat:w3uigrxom5c4vnsaepdgnpbli4

A Survey of Information Retrieval Techniques

Mang'are Fridah Nyamisa
2017 Advances in Networks  
This understanding is vital as it facilitates the choice of an information retrieval technique, based on the underlying requirements.  ...  The results of this survey revealed that the current information retrieval models fall short of the expectations in one way or the other.  ...  According to [33] , Divergence-From-Randomness Model is one type of probabilistic model where term weights are calculated by determining the divergence between a term distribution produced by a random  ... 
doi:10.11648/j.net.20170502.12 fatcat:i3ahtt53bfd2rcc5aytt6jvb6e

A comparison of various approaches for using probabilistic dependencies in language modeling

Peter Bruza, Dawei Song
2003 Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR '03  
Acknowledgements The work reported in this paper has been funded in part by the Cooperative Research Centres Program through the Department of the Prime Minister and Cabinet of Australia.  ...  The associated number with each model is its KL divergence from the true relevance model. where ) (w tf divergence of an estimate e P of the relevance model and the true model R P : ∑ =  ...  INTRODUCTION The Relevance-based language model is a promising invention within the language modeling approach to document retrieval [4] .  ... 
doi:10.1145/860435.860530 dblp:conf/sigir/BruzaS03 fatcat:qflb5hvprfhwzontgxm6wzwn3y

A comparison of various approaches for using probabilistic dependencies in language modeling

Peter Bruza, Dawei Song
2003 Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR '03  
Acknowledgements The work reported in this paper has been funded in part by the Cooperative Research Centres Program through the Department of the Prime Minister and Cabinet of Australia.  ...  The associated number with each model is its KL divergence from the true relevance model. where ) (w tf divergence of an estimate e P of the relevance model and the true model R P : ∑ =  ...  INTRODUCTION The Relevance-based language model is a promising invention within the language modeling approach to document retrieval [4] .  ... 
doi:10.1145/860500.860530 fatcat:rr7dsj2ylffatisd4owckgiwzq
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