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Document selection methodologies for efficient and effective learning-to-rank
2009
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09
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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
2017
2017 IEEE International Conference on Big Data (Big Data)
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2019 pre-print arXiv:1904.01719v1 fatcat:n4fl5f5btrgdzbbb66f54uw3k4
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
2017
IJARCCE
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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]
2011
Lecture Notes in Computer Science
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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
2018
Zenodo
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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
2018
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18
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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)
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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
2013
Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13
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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
2019
Sadhana (Bangalore)
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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
2016
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16
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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]
2018
arXiv
pre-print
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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
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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
2012
Information retrieval (Boston)
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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
2018
Zenodo
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., 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]
2005
Lecture Notes in Computer Science
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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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