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Document selection methodologies for efficient and effective learning-to-rank

Javed A. Aslam, Evangelos Kanoulas, Virgil Pavlu, Stefan Savev, Emine Yilmaz
2009 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09  
However, relatively little research has been conducted on selecting documents for learning-to-rank data sets nor on the effect of these choices on the efficiency and effectiveness of learning-to-rank algorithms  ...  We investigate whether they can also enable efficient and effective learningto-rank. We compare them with the document selection methodology used to create the LETOR datasets.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1145/1571941.1572022 dblp:conf/sigir/AslamKPSY09 fatcat:7eunxfursfdjfh4drowqjygcbu

Empirical evaluations of active learning strategies in legal document review

Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling, Jianping Zhang, Haozhen Zhao
2017 2017 IEEE International Conference on Big Data (Big Data)  
The purpose of this paper is twofold: (i) to question whether Active Learning actually is a superior learning methodology and (ii) to highlight the ways that Active Learning can be most effectively applied  ...  documents are left for review.  ...  CONCLUSIONS Active Learning has drawn the attention of the legal community because of its potential to make the predictive coding process even more effective and efficient.  ... 
doi:10.1109/bigdata.2017.8258076 dblp:conf/bigdataconf/ChhatwalHKZZ17 fatcat:3vmjeqsg7rgm5lhxj6yi5khoxq

"Active Learning for Ranking through Expected Loss Optimization" based on Data Mining

Parkhi Aishwarya, Patil Chaitanya, Shinde Madhuri
2017 IJARCCE  
Then, we investigate both query and document level active learning for raking and propose a twostage ELO-DCG algorithm which incorporate both query and document selection into active learning.  ...  This presents a great need for the active learning approaches to select most informative examples for ranking learning; however, in the literature there is still very limited work to address active learning  ...  Authors-Monika M Patel a, Mehul A Jajal, Dixita B vataliya Document Selection Methodologies for Efficient and Effective Learning-to-Rank Document selection methodology employ a number of document selection  ... 
doi:10.17148/ijarcce.2017.6590 fatcat:o7krw7jtivcxhir6b5yhfuigke

The Importance of the Depth for Text-Image Selection Strategy in Learning-To-Rank [chapter]

David Buffoni, Sabrina Tollari, Patrick Gallinari
2011 Lecture Notes in Computer Science  
Task: produce a sorted list of images given a user query Problem: how to efficiently learn a ranking function ?  ...  Our work: evaluation of the impact of the depth of a pooling methodology on Learning-to-Rank (LTR) algorithms.  ...  Baseline and Learning-To-Rank Models Baseline: BL(q, d) = λS T ext (q t , d t )+(1−λ)S V isual (q v , d v ) where S T ext (q t , d t ) = BM 25 and S V isual (q v , d v ) = max qi (Histo HSV (q i , d i  ... 
doi:10.1007/978-3-642-20161-5_84 fatcat:6vdnk7dravdezocvfwuzbgdnpe

Selective Gradient Boosting for Effective Learning to Rank

Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani
2018 Zenodo  
Learning an effective ranking function from a large number of query-document examples is a challenging task.  ...  In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely  ...  We also acknowledge the support of Istella S.p.A., and, in particular, of Domenico Dato and Monica Mori.  ... 
doi:10.5281/zenodo.2668013 fatcat:tgoaimsigvc2lbqpp22w2tdx6q

Selective Gradient Boosting for Effective Learning to Rank

Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani
2018 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18  
Learning an effective ranking function from a large number of query-document examples is a challenging task.  ...  In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely  ...  We also acknowledge the support of Istella S.p.A., and, in particular, of Domenico Dato and Monica Mori.  ... 
doi:10.1145/3209978.3210048 dblp:conf/sigir/LuccheseN00T18 fatcat:6evdwifa3vf7phim4lyz7jsmk4

Ranking Models and Learning to Rank: A Survey

2015 International Journal of Science and Research (IJSR)  
This paper mainly focuses on survey of the ranking models and learning to rank technique for giving the effective and efficient information retrieval.  ...  As a result, effective and efficient information retrieval is being more important and also search engine (information retrieval system) has turned out to be a vital tool for people to locate their needed  ...  The comparison of relevant document selection methodologies in learning to rank are found in [8] . L. Yang, L.  ... 
doi:10.21275/v4i12.nov151889 fatcat:svwiztlpr5e2zb2atqaraj5pxy

Efficient and effective retrieval using selective pruning

Nicola Tonellotto, Craig Macdonald, Iadh Ounis
2013 Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13  
We thoroughly experiment to ascertain the efficiency and effectiveness impacts of the proposed approaches, as part of a search engine deploying state-of-the-art learning to rank techniques.  ...  Retrieval can be made more efficient by deploying dynamic pruning strategies such as Wand, which do not degrade effectiveness up to a given rank.  ...  Figure 5 : 5 Effectiveness of the sample and learned models for different F and K. Figure 6 : 6 Comparison of efficiency and effectiveness for Hypotheses 1& 2 selective pruning approaches.  ... 
doi:10.1145/2433396.2433407 dblp:conf/wsdm/TonellottoMO13 fatcat:nv2qvk3ihng4hltyqbei2i4jsi

Identification of efficient algorithms for web search through implementation of learning-to-rank algorithms

Nikhil Dhake, Shital Raut, Ashwini Rahangdale
2019 Sadhana (Bangalore)  
Machine learning domain called learning-to-rank comes to the aid to rank the obtained results. Different state-of-the-art methodologies have been developed for learning-to-rank to date.  ...  Our work in this paper marks the implementation of learning-to-rank algorithms and analyses effect of topmost performing algorithms on respective datasets.  ...  It sets the strategy to search an efficient algorithm for the web search document retrieval using implementation of learning-to-rank and analyses the performance of existing standard learning-to-rank algorithms  ... 
doi:10.1007/s12046-019-1073-5 fatcat:j7hcmnjcwjaujcqlzmbgfgims4

Post-Learning Optimization of Tree Ensembles for Efficient Ranking

Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri, Salvatore Trani
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ranking functions from a ground-truth of training examples.  ...  In practice, efficiency and effectiveness are intertwined concepts and trading off effectiveness for meeting efficiency constraints typically existing in large-scale systems is one of the most urgent issues  ...  Nowadays, Learning-to-Rank (LtR) [6] methodologies are pervasively used as effective solutions to the most difficult ranking problems.  ... 
doi:10.1145/2911451.2914763 dblp:conf/sigir/LuccheseNOPST16 fatcat:wzt4avnpbbfjbdbu5gnssemryq

A Deep Cascade Model for Multi-Document Reading Comprehension [article]

Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen
2018 arXiv   pre-print
document selection and paragraph ranking.  ...  To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction  ...  Implementation Details For the cascade ranking functions, the number of selected documents K and paragraphs N are the key factors to balance the effectiveness and efficiency trade-off.  ... 
arXiv:1811.11374v1 fatcat:3jtulmdoyjfd3ltxjrnyoy7liu

A Deep Cascade Model for Multi-Document Reading Comprehension

Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
document selection and paragraph ranking.  ...  A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system.  ...  Implementation Details For the cascade ranking functions, the number of selected documents K and paragraphs N are the key factors to balance the effectiveness and efficiency trade-off.  ... 
doi:10.1609/aaai.v33i01.33017354 fatcat:lo52vbvoaffjrbbcqloic2kxny

The whens and hows of learning to rank for web search

Craig Macdonald, Rodrygo L. T. Santos, Iadh Ounis
2012 Information retrieval (Boston)  
However, the properties of the document sample such as when to stop ranking -i.e. its minimum effective size -remain unstudied.  ...  For instance, a sample of documents with sufficient recall is used, such that reranking of the sample by the learned model brings the relevant documents to the top.  ...  We also acknowledge and thank the authors of the open source learning to rank tools that were used in the experiments of this article.  ... 
doi:10.1007/s10791-012-9209-9 fatcat:bsyiwzf4xzc5zfwifokwpwh6rm

Efficient and Effective Query Expansion for Web Search

Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Roberto Trani, Rossano Venturini
2018 Zenodo  
., synonyms and acronyms, to enhance the system recall. State-of-the-art solutions employ machine learning methods to select the most suitable terms.  ...  In particular, the proposed expansion selection strategies aim at capturing the efficiency and the effectiveness of the expansion candidates, as well as the dependencies among them.  ...  and Communication Technologies programme.  ... 
doi:10.5281/zenodo.2668249 fatcat:d7khj6dc6rafzdim4ewlxrfsgy

Federated Search of Text-Based Digital Libraries in Hierarchical Peer-to-Peer Networks [chapter]

Jie Lu, Jamie Callan
2005 Lecture Notes in Computer Science  
This paper addresses the problems of resource representation, resource ranking and selection, and result merging for federated search of text-based digital libraries in hierarchical peer-to-peer networks  ...  Existing approaches to text-based federated search are adapted and two new methods are developed for resource representation and resource selection according to the unique characteristics of hierarchical  ...  ACKNOWLEDGMENTS This material is based on work supported by NSF grant IIS-0118767 and IIS-0240334.  ... 
doi:10.1007/978-3-540-31865-1_5 fatcat:ldndnza3ebhapd2wtdflsao2oq
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