2021
Towards More Accountable Search Engines: Online Evaluation of Representation Bias Journal Article
Lipani, Aldo; Piroi, Florina; Yilmaz, Emine
In: arXiv preprint arXiv:2110.08835, 2021.
@article{lipani2021towards,
title = {Towards More Accountable Search Engines: Online Evaluation of Representation Bias},
author = {Aldo Lipani and Florina Piroi and Emine Yilmaz},
url = {https://arxiv.org/abs/2110.08835},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {arXiv preprint arXiv:2110.08835},
abstract = {Information availability affects people's behavior and perception of the world. Notably, people rely on search engines to satisfy their need for information. Search engines deliver results relevant to user requests usually without being or making themselves accountable for the information they deliver, which may harm people's lives and, in turn, society. This potential risk urges the development of evaluation mechanisms of bias in order to empower the user in judging the results of search engines. In this paper, we give a possible solution to measuring representation bias with respect to societal features for search engines and apply it to evaluating the gender representation bias for Google's Knowledge Graph Carousel for listing occupations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
A Multilinear Sampling Algorithm to Estimate Shapley Values Proceedings Article
Okhrati, Ramin; Lipani, Aldo
In: Proc. of ICPR, 2020.
@inproceedings{okhrati2020multilinear,
title = {A Multilinear Sampling Algorithm to Estimate Shapley Values},
author = {Ramin Okhrati and Aldo Lipani},
url = {https://www.researchgate.net/publication/344825957_A_Multilinear_Sampling_Algorithm_to_Estimate_Shapley_Values},
year = {2020},
date = {2020-10-25},
booktitle = {Proc.~of ICPR},
series = {ICPR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Predicting Engagement in Video Lectures Proceedings Article
Bulathwela, Sahan; Perez-Ortiz, Maria; Lipani, Aldo; Yilmaz, Emine; Shawe-Taylor, John
In: Proc. of EDM, 2020.
@inproceedings{bulathwela2020predictingb,
title = {Predicting Engagement in Video Lectures},
author = {Sahan Bulathwela and Maria Perez-Ortiz and Aldo Lipani and Emine Yilmaz and John Shawe-Taylor},
url = {https://www.researchgate.net/publication/344832087_Predicting_Engagement_in_Video_Lectures},
year = {2020},
date = {2020-01-01},
booktitle = {Proc.~of EDM},
series = {EDM '20},
abstract = {The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and per-sonalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Self-Attentive Hawkes Processes Proceedings Article
Zhang, Qiang; Lipani, Aldo; Kirnap, Omer; Yilmaz, Emine
In: Proc. of ICML, 2020.
@inproceedings{zhang2020selfattentiveb,
title = {Self-Attentive Hawkes Processes},
author = {Qiang Zhang and Aldo Lipani and Omer Kirnap and Emine Yilmaz},
url = {https://www.researchgate.net/publication/344832377_Self-Attentive_Hawkes_Process},
year = {2020},
date = {2020-01-01},
booktitle = {Proc.~of ICML},
series = {ICML '20},
abstract = {Capturing the occurrence dynamics is crucial to predicting which type of events will happen next and when. A common method to do this is through Hawkes processes. To enhance their capacity, recurrent neural networks (RNNs) have been incorporated due to RNNs’ successes in processing sequential data such as languages. Recent evidence suggests that self-attention is more competent than RNNs in dealing with languages. However, we are unaware of the effectiveness of self-attention in the context of Hawkes processes. This study aims to fill the gap by designing a self-attentive Hawkes process (SAHP). SAHP employs self-attention to summarise the influence of history events and compute the probability of the next event. One deficit of the conventional self-attention when applied to event sequences is that its positional encoding only considers the order of a sequence ignoring the time intervals between events. To overcome this deficit, we modify its encoding by translating time intervals into phase shifts of sinusoidal functions. Experiments on goodness-of-fit and prediction tasks show the improved capability of SAHP. Furthermore, SAHP is more interpretable than RNN-based counterparts because the learnt attention weights reveal contributions of one event type to the happening of another type. To the best of our knowledge, this is the first work that studies the effectiveness of self-attention in Hawkes processes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Easing Legal News Monitoring with Learning to Rank and BERT Proceedings Article
Sanchez, Luis; He, Jiyin; Manotumruksa, Jarana; Albakour, Dyaa; Martinez, Miguel; Lipani, Aldo
In: Proc. of ECIR, pp. 336–343, Springer International Publishing, Cham, 2020.
@inproceedings{10.1007/978-3-030-45442-5_42,
title = {Easing Legal News Monitoring with Learning to Rank and BERT},
author = {Luis Sanchez and Jiyin He and Jarana Manotumruksa and Dyaa Albakour and Miguel Martinez and Aldo Lipani},
url = {https://www.researchgate.net/publication/338825714_Easing_Legal_News_Monitoring_with_Learning_to_Rank_and_BERT},
year = {2020},
date = {2020-01-01},
booktitle = {Proc.~of ECIR},
pages = {336--343},
publisher = {Springer International Publishing},
address = {Cham},
series = {ECIR '20},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Learning to Re-Rank with Contextualized Stopwords Proceedings Article
Hofstätter, Sebastian; Lipani, Aldo; Zlabinger, Markus; Hanbury, Allan
In: Proc. of CIKM, 2020.
@inproceedings{hofstaetter2020learning,
title = {Learning to Re-Rank with Contextualized Stopwords},
author = {Sebastian Hofst\"{a}tter and Aldo Lipani and Markus Zlabinger and Allan Hanbury},
url = {https://www.researchgate.net/publication/344832244_Learning_to_Re-Rank_with_Contextualized_Stopwords},
year = {2020},
date = {2020-01-01},
booktitle = {Proc.~of CIKM},
series = {CIKM '20},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2019
FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval Journal Article
Roegiest, Adam; Lipani, Aldo; Beutel, Alex; Olteanu, Alexandra; Lucic, Ana; Stoica, Ana-Andreea; Das, Anubrata; Biega, Asia; Voorn, Bart; Hauff, Claudia; Spina, Damiano; Lewis, David; Oard, Douglas W; Yilmaz, Emine; Hasibi, Faegheh; Kazai, Gabriella; McDonald, Graham; Haned, Hinda; Ounis, Iadh; van der Linden, Ilse; Garcia-Gathright, Jean; Baan, Joris; Lau, Kamuela N; Balog, Krisztian; de Rijke, Maarten; Sayed, Mahmoud; Panteli, Maria; Sanderson, Mark; Lease, Matthew; Ekstrand, Michael D; Lahoti, Preethi; Kamishima, Toshihiro
In: SIGIR Forum, vol. 53, no. 2, pp. 20–43, 2019.
@article{roegiest-2019-facts-ir,
title = {FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval},
author = {Adam Roegiest and Aldo Lipani and Alex Beutel and Alexandra Olteanu and Ana Lucic and Ana-Andreea Stoica and Anubrata Das and Asia Biega and Bart Voorn and Claudia Hauff and Damiano Spina and David Lewis and Douglas W Oard and Emine Yilmaz and Faegheh Hasibi and Gabriella Kazai and Graham McDonald and Hinda Haned and Iadh Ounis and Ilse van der Linden and Jean Garcia-Gathright and Joris Baan and Kamuela N Lau and Krisztian Balog and Maarten de Rijke and Mahmoud Sayed and Maria Panteli and Mark Sanderson and Matthew Lease and Michael D Ekstrand and Preethi Lahoti and Toshihiro Kamishima},
url = {https://www.researchgate.net/publication/337933046_FACTS-IR_Fairness_Accountability_Confidentiality_Transparency_and_Safety_in_Information_Retrieval},
year = {2019},
date = {2019-12-01},
journal = {SIGIR Forum},
volume = {53},
number = {2},
pages = {20--43},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fixed-Cost Pooling Strategies Journal Article
Lipani, Aldo; Losada, David E.; Zuccon, Guido; Lupu, Mihai
In: IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
@article{Lipani2019TKDE,
title = {Fixed-Cost Pooling Strategies},
author = {Aldo Lipani and David E. Losada and Guido Zuccon and Mihai Lupu},
url = {https://www.researchgate.net/publication/336369355_Fixed-Cost_Pooling_Strategies},
year = {2019},
date = {2019-10-06},
journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
abstract = {The empirical nature of Information Retrieval (IR) mandates strong experimental practices. A keystone of such experimental practices is the Cranfield evaluation paradigm. Within this paradigm, the collection of relevance judgments has been the subject of intense scientific investigation. This is because, on one hand, consistent, precise, and numerous judgements are keys to reducing evaluation uncertainty and test collection bias; on the other hand, however, relevance judgements are costly to collect. The selection of which documents to judge for relevance, known as pooling method, has therefore a great impact on IR evaluation. In this paper we focus on the bias introduced by the pooling method, known as pool bias, which affects the reusability of test collections, in particular when building test collections with a limited budget. In this paper we formalize and evaluate a set of 22 pooling strategies based on: traditional strategies, voting systems, retrieval fusion methods, evaluation measures, and multi-armed bandit models. To do this we run a large-scale evaluation by considering a set of 9 standard TREC test collections, in which we show that the choice of the pooling strategy has significant effects on the cost needed to obtain an unbiased test collection. We also identify the least biased pooling strategy in terms of pool bias according to three IR evaluation measures: AP, NDCG, and P@10.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
From a User Model for Query Sessions to Session Rank Biased Precision (sRBP) Proceedings Article
Lipani, Aldo; Carterette, Ben; Yilmaz, Emine
In: Proc. of ICTIR, 2019.
@inproceedings{Lipani2019,
title = {From a User Model for Query Sessions to Session Rank Biased Precision (sRBP)},
author = {Aldo Lipani and Ben Carterette and Emine Yilmaz},
url = {https://www.researchgate.net/publication/334725760_From_a_User_Model_for_Query_Sessions_to_Session_Rank_Biased_Precision_sRBP},
doi = {10.1145/3341981.3344216},
year = {2019},
date = {2019-10-02},
booktitle = {Proc.~of ICTIR},
journal = {Proc.~of ICTIR},
abstract = {To satisfy their information needs, users usually carry out searches on retrieval systems by continuously trading off between the examination of search results retrieved by under-specified queries and the refinement of these queries through reformulation. In Information Retrieval (IR), a series of query reformulations is known as a query-session. Research in IR evaluation has traditionally been focused on the development of measures for the ad hoc task, for which a retrieval system aims to retrieve the best documents for a single query. Thus, most IR evaluation measures, with a few exceptions , are not suitable to evaluate retrieval scenarios that call for multiple refinements over a query-session. In this paper, by formally modeling a user's expected behaviour over query-sessions, we derive a session-based evaluation measure, which results in a generalization of the evaluation measure Rank Biased Precision (RBP). We demonstrate the quality of this new session-based evaluation measure, named Session RBP (sRBP), by evaluating its user model against the observed user behaviour over the query-sessions of the 2014 TREC Session track.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Reply-aided Detection of Misinformation via Bayesian Deep Learning Proceedings Article
Zhang, Qiang; Lipani, Aldo; Liang, Shangsong; Yilmaz, Emine
In: Proc. of WWW, 2019.
@inproceedings{Zhang2019b,
title = {Reply-aided Detection of Misinformation via Bayesian Deep Learning},
author = {Qiang Zhang and Aldo Lipani and Shangsong Liang and Emine Yilmaz},
doi = {10.1145/3308558.3313718},
year = {2019},
date = {2019-05-13},
booktitle = {Proc.~of WWW},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
From Stances’ Imbalance to Their Hierarchical Representation and Detection Proceedings Article
Zhang, Qiang; Liang, Shangsong; Lipani, Aldo; Ren, Zhaochun; Yilmaz, Emine
In: Proc. of WWW, 2019.
@inproceedings{Zhang2019b,
title = {From Stances’ Imbalance to Their Hierarchical Representation and Detection},
author = {Qiang Zhang and Shangsong Liang and Aldo Lipani and Zhaochun Ren and Emine Yilmaz},
doi = {10.1145/3308558.3313724},
year = {2019},
date = {2019-05-13},
booktitle = {Proc.~of WWW},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
On Biases in Information Retrieval Models and Evaluation Journal Article
Lipani, Aldo
In: SIGIR Forum, vol. 52, no. 2, 2019.
@article{Lipani:2019:BIR:3308774.3308804,
title = {On Biases in Information Retrieval Models and Evaluation},
author = {Aldo Lipani},
doi = {10.1145/3308774.3308804},
year = {2019},
date = {2019-01-01},
journal = {SIGIR Forum},
volume = {52},
number = {2},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
On the Learnability of Software Router Performance via CPU Measurements Proceedings Article
Shelbourne, Charles; Linguaglossa, Leonardo; Lipani, Aldo; Zhang, Tianzhu; Geyer, Fabien
In: Proc. of CoNEXT, pp. 23–25, Association for Computing Machinery, Orlando, FL, USA, 2019, ISBN: 9781450370066.
@inproceedings{10.1145/3360468.3366776,
title = {On the Learnability of Software Router Performance via CPU Measurements},
author = {Charles Shelbourne and Leonardo Linguaglossa and Aldo Lipani and Tianzhu Zhang and Fabien Geyer},
url = {https://www.researchgate.net/publication/337580746_On_the_Learnability_of_Software_Router_Performance_via_CPU_Measurements
https://doi.org/10.1145/3360468.3366776
},
doi = {10.1145/3360468.3366776},
isbn = {9781450370066},
year = {2019},
date = {2019-01-01},
booktitle = {Proc.~of CoNEXT},
pages = {23\textendash25},
publisher = {Association for Computing Machinery},
address = {Orlando, FL, USA},
series = {CoNEXT ’19},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
On Biases in Information Retrieval Models and Evaluation PhD Thesis
Lipani, Aldo
TU Wien, 2018.
@phdthesis{PhDLipani2018,
title = {On Biases in Information Retrieval Models and Evaluation},
author = {Aldo Lipani},
url = {http://aldolipani.com/wp-content/uploads/2018/09/phd_thesis.pdf},
doi = {10.13140/RG.2.2.28623.74400},
year = {2018},
date = {2018-09-21},
school = {TU Wien},
abstract = {The advent of the modern information technology has benefited society as the digitisation of content increased over the last half-century. While the processing capability of our species has remained unchanged, the information available to us has been notably increasing. In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating relevant information from the rest. This separation, however, is a difficult task rooted in the complexity of understanding of what is and what is not relevant. To manage this complexity, IR has developed a strong empirical nature, which has led to the development of grounded retrieval models, resulting in the development of retrieval systems empirically designed to be biased towards relevant information. However, other biases have been observed, which counteract retrieval performance. In this thesis, the reduction of retrieval systems to filters of information, or sampling processes, has allowed us to systematically investigate these biases.
We study biases manifesting in two aspects of IR research: retrieval models and retrieval evaluation. We start by identifying retrieval biases in probabilistic IR models and then develop new document priors to improve retrieval performance. Next, we discuss the accessibility bias of retrieval models, and for Boolean retrieval models we develop a mathematical framework of retrievability. For retrieval evaluation biases, we study how test collections are built using the pooling method and how this method introduces bias. Then, to improve the reliability of the evaluation, we first develop new pooling strategies to mitigate this bias at test collection build time and then, for two IR evaluation measures, Precision and Recall at cut-off (P@n and R@n), we develop new pool bias estimators to mitigate it at evaluation time.
Through a large scale experimentation involving up to 15 test collections, four IR evaluation measures and three bias measures, we demonstrate that including document priors based on verboseness improves the performance of probabilistic retrieval models; that the accessibility bias of Boolean retrieval models quickly worsens for conjunctive queries with the increase of the query length (while slightly improving for disjunctive queries); that the test collection bias can be lowered at test collection build time by pooling strategies inspired by a well-known problem in reinforcement learning, the multi-armed bandit problem; and that this bias can also be improved at evaluation time by analysing the runs participating in the pool. For this last point in particular, we show that for P@n, bias reduction is done by quantifying the potential of the new system against the pooled runs, and for R@n, this is done instead by simulating the absence of a pooled run from the set of pooled runs.
This thesis contributes to the IR field by giving a better understanding of relevance through the lens of biases in retrieval models and retrieval evaluation. The identification of these biases, and their exploitation or mitigation, leads to the development of better performing IR models and the improvement of the current IR evaluation practice.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
We study biases manifesting in two aspects of IR research: retrieval models and retrieval evaluation. We start by identifying retrieval biases in probabilistic IR models and then develop new document priors to improve retrieval performance. Next, we discuss the accessibility bias of retrieval models, and for Boolean retrieval models we develop a mathematical framework of retrievability. For retrieval evaluation biases, we study how test collections are built using the pooling method and how this method introduces bias. Then, to improve the reliability of the evaluation, we first develop new pooling strategies to mitigate this bias at test collection build time and then, for two IR evaluation measures, Precision and Recall at cut-off (P@n and R@n), we develop new pool bias estimators to mitigate it at evaluation time.
Through a large scale experimentation involving up to 15 test collections, four IR evaluation measures and three bias measures, we demonstrate that including document priors based on verboseness improves the performance of probabilistic retrieval models; that the accessibility bias of Boolean retrieval models quickly worsens for conjunctive queries with the increase of the query length (while slightly improving for disjunctive queries); that the test collection bias can be lowered at test collection build time by pooling strategies inspired by a well-known problem in reinforcement learning, the multi-armed bandit problem; and that this bias can also be improved at evaluation time by analysing the runs participating in the pool. For this last point in particular, we show that for P@n, bias reduction is done by quantifying the potential of the new system against the pooled runs, and for R@n, this is done instead by simulating the absence of a pooled run from the set of pooled runs.
This thesis contributes to the IR field by giving a better understanding of relevance through the lens of biases in retrieval models and retrieval evaluation. The identification of these biases, and their exploitation or mitigation, leads to the development of better performing IR models and the improvement of the current IR evaluation practice.
A Systematic Approach to Normalization in Probabilistic Models Journal Article
Lipani, Aldo; Roelleke, Thomas; Lupu, Mihai; Hanbury, Allan
In: Information Retrieval Journal, 2018.
@article{Lipani2018,
title = {A Systematic Approach to Normalization in Probabilistic Models},
author = {Aldo Lipani and Thomas Roelleke and Mihai Lupu and Allan Hanbury},
doi = {10.1007/s10791-018-9334-1},
year = {2018},
date = {2018-06-30},
journal = {Information Retrieval Journal},
abstract = {Every information retrieval (IR) model embeds in its scoring function a form of term frequency (TF) quantification. The contribution of the term frequency is determined by the properties of the function of the chosen TF quantification, and by its TF normalization. The first defines how independent the occurrences of multiple terms are, while the second acts on mitigating the a priori probability of having a high term frequency in a document (estimation usually based on the document length). New test collections, coming from different domains (e.g. medical, legal), give evidence that not only document length, but in addition, verboseness of documents should be explicitly considered. Therefore we propose and investigate a systematic combination of document verboseness and length. To theoretically justify the combination, we show the duality between document verboseness and length. In addition, we investigate the duality between verboseness and other components of IR models. We test these new TF normalizations on four suitable test collections. We do this on a well defined spectrum of TF quantifications. Finally, based on the theoretical and experimental observations, we show how the two components of this new normalization, document verboseness and length, interact with each other. Our experiments demonstrate that the new models never underperform existing models, while sometimes introducing statistically significantly better results, at no additional computational cost.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2017
Fixed-Cost Pooling Strategies Based on IR Evaluation Measures Proceedings Article
Lipani, Aldo; Palotti, Joao; Lupu, Mihai; Piroi, Florina; Zuccon, Guido; Hanbury, Allan
In: Proc. of ECIR, 2017.
@inproceedings{Lipani2017,
title = {Fixed-Cost Pooling Strategies Based on IR Evaluation Measures},
author = {Aldo Lipani and Joao Palotti and Mihai Lupu and Florina Piroi and Guido Zuccon and Allan Hanbury},
doi = {10.1007/978-3-319-56608-5_28},
year = {2017},
date = {2017-01-01},
booktitle = {Proc.~of ECIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Visual Pool: A Tool to Visualize and Interact with the Pooling Method Proceedings Article
Lipani, Aldo; Lupu, Mihai; Hanbury, Allan
In: Proc. of SIGIR, 2017.
@inproceedings{Lipani:2017:VPT:3077136.3084146,
title = {Visual Pool: A Tool to Visualize and Interact with the Pooling Method},
author = {Aldo Lipani and Mihai Lupu and Allan Hanbury},
doi = {10.1145/3077136.3084146},
year = {2017},
date = {2017-01-01},
booktitle = {Proc.~of SIGIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Fixed Budget Pooling Strategies Based on Fusion Methods Proceedings Article
Lipani, Aldo; Lupu, Mihai; Palotti, Joao; Zuccon, Guido; Hanbury, Allan
In: Proc. of SAC, 2017.
@inproceedings{Lipani:2017:FBP:3019612.3019692,
title = {Fixed Budget Pooling Strategies Based on Fusion Methods},
author = {Aldo Lipani and Mihai Lupu and Joao Palotti and Guido Zuccon and Allan Hanbury},
doi = {10.1145/3019612.3019692},
year = {2017},
date = {2017-01-01},
booktitle = {Proc.~of SAC},
abstract = {The empirical nature of Information Retrieval (IR) mandates strong experimental practices. The Cranfield/TREC evaluation paradigm represents a keystone of such experimental practices. Within this paradigm, the generation of relevance judgments has been the subject of intense scientific investigation. This is because, on one hand, consistent, precise and numerous judgements are key to reduce evaluation uncertainty and test collection bias; on the other hand, however, relevance judgements are costly to collect. The selection of which documents to judge for relevance (known as pooling) has therefore great impact in IR evaluation. In this paper, we contribute a set of 8 novel pooling strategies based on retrieval fusion methods. We show that the choice of the pooling strategy has significant effects on the cost needed to obtain an unbiased test collection; we also identify the best performing pooling strategy according to three evaluation measure.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Schillaci, Calogero; Acutis, Marco; Lombardo, Luigi; Lipani, Aldo; Fantappiè, Maria; Märker, Michael; Saia, Sergio
In: Science of The Total Environment, vol. 601-602, 2017.
@article{SCHILLACI2017821,
title = {Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling},
author = {Calogero Schillaci and Marco Acutis and Luigi Lombardo and Aldo Lipani and Maria Fantappi\`{e} and Michael M\"{a}rker and Sergio Saia},
doi = {10.1016/j.scitotenv.2017.05.239},
year = {2017},
date = {2017-01-01},
journal = {Science of The Total Environment},
volume = {601-602},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2016
Fairness in Information Retrieval Proceedings Article
Lipani, Aldo
In: Proc. of SIGIR, 2016.
@inproceedings{Lipani:2016:FIR:2911451.2911473,
title = {Fairness in Information Retrieval},
author = {Aldo Lipani},
doi = {10.1145/2911451.2911473},
year = {2016},
date = {2016-01-01},
booktitle = {Proc.~of SIGIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Solitude of Relevant Documents in the Pool Proceedings Article
Lipani, Aldo; Lupu, Mihai; Kanoulas, Evangelos; Hanbury, Allan
In: Proc. of CIKM, 2016.
@inproceedings{Lipani:2016:SRD:2983323.2983891,
title = {The Solitude of Relevant Documents in the Pool},
author = {Aldo Lipani and Mihai Lupu and Evangelos Kanoulas and Allan Hanbury},
doi = {10.1145/2983323.2983891},
year = {2016},
date = {2016-01-01},
booktitle = {Proc.~of CIKM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Curious Incidence of Bias Corrections in the Pool Proceedings Article
Lipani, Aldo; Lupu, Mihai; Hanbury, Allan
In: Proc. of ECIR, 2016.
@inproceedings{Lipani2016,
title = {The Curious Incidence of Bias Corrections in the Pool},
author = {Aldo Lipani and Mihai Lupu and Allan Hanbury},
doi = {10.1007/978-3-319-30671-1_20},
year = {2016},
date = {2016-01-01},
booktitle = {Proc.~of ECIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Impact of Fixed-Cost Pooling Strategies on Test Collection Bias Proceedings Article
Lipani, Aldo; Zuccon, Guido; Lupu, Mihai; Koopman, Bevan; Hanbury, Allan
In: Proc. of ICTIR, 2016.
@inproceedings{Lipani:2016:IFP:2970398.2970429,
title = {The Impact of Fixed-Cost Pooling Strategies on Test Collection Bias},
author = {Aldo Lipani and Guido Zuccon and Mihai Lupu and Bevan Koopman and Allan Hanbury},
doi = {10.1145/2970398.2970429},
year = {2016},
date = {2016-01-01},
booktitle = {Proc.~of ICTIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2015
DASyR(IR) - Document Analysis System for Systematic Reviews (in Information Retrieval) Proceedings Article
Piroi, Florina; Lipani, Aldo; Lupu, Mihai; Hanbury, Allan
In: Proc. of ICDAR, 2015.
@inproceedings{7333830,
title = {DASyR(IR) - Document Analysis System for Systematic Reviews (in Information Retrieval)},
author = {Florina Piroi and Aldo Lipani and Mihai Lupu and Allan Hanbury},
doi = {10.1109/ICDAR.2015.7333830},
year = {2015},
date = {2015-08-01},
booktitle = {Proc.~of ICDAR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Verboseness Fission for BM25 Document Length Normalization Proceedings Article
Lipani, Aldo; Lupu, Mihai; Hanbury, Allan; Aizawa, Akiko
In: Proc. of ICTIR, 2015.
@inproceedings{Lipani:2015:VFB:2808194.2809486,
title = {Verboseness Fission for BM25 Document Length Normalization},
author = {Aldo Lipani and Mihai Lupu and Allan Hanbury and Akiko Aizawa},
doi = {10.1145/2808194.2809486},
year = {2015},
date = {2015-01-01},
booktitle = {Proc.~of ICTIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
An Initial Analytical Exploration of Retrievability Proceedings Article
Lipani, Aldo; Lupu, Mihai; Aizawa, Akiko; Hanbury, Allan
In: Proc. of ICTIR, 2015.
@inproceedings{Lipani:2015:IAE:2808194.2809495,
title = {An Initial Analytical Exploration of Retrievability},
author = {Aldo Lipani and Mihai Lupu and Akiko Aizawa and Allan Hanbury},
doi = {10.1145/2808194.2809495},
year = {2015},
date = {2015-01-01},
booktitle = {Proc.~of ICTIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Splitting Water: Precision and Anti-Precision to Reduce Pool Bias Proceedings Article
Lipani, Aldo; Lupu, Mihai; Hanbury, Allan
In: Proc. of SIGIR, 2015.
@inproceedings{Lipani:2015:SWP:2766462.2767749,
title = {Splitting Water: Precision and Anti-Precision to Reduce Pool Bias},
author = {Aldo Lipani and Mihai Lupu and Allan Hanbury},
doi = {10.1145/2766462.2767749},
year = {2015},
date = {2015-01-01},
booktitle = {Proc.~of SIGIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
TUW-IMP at the NTCIR-11 Math-2 Proceedings Article
Lipani, Aldo; Andersson, Linda; Piroi, Florina; Lupu, Mihai; Hanbury, Allan
In: Proc. of NTCIR, 2014.
@inproceedings{Lipani2014TUWIMPAT,
title = {TUW-IMP at the NTCIR-11 Math-2},
author = {Aldo Lipani and Linda Andersson and Florina Piroi and Mihai Lupu and Allan Hanbury},
doi = {10.13140/2.1.1127.8404},
year = {2014},
date = {2014-01-01},
booktitle = {Proc.~of NTCIR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Extracting Nanopublications from IR Papers Proceedings Article
Lipani, Aldo; Piroi, Florina; Andersson, Linda; Hanbury, Allan
In: Proc. of IRFC, 2014.
@inproceedings{Lipani2014b,
title = {Extracting Nanopublications from IR Papers},
author = {Aldo Lipani and Florina Piroi and Linda Andersson and Allan Hanbury},
doi = {10.1007/978-3-319-12979-2_5},
year = {2014},
date = {2014-01-01},
booktitle = {Proc.~of IRFC},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
An Information Retrieval Ontology for Information Retrieval Nanopublications Proceedings Article
Lipani, Aldo; Piroi, Florina; Andersson, Linda; Hanbury, Allan
In: Proc. of CLEF, 2014.
@inproceedings{Lipani2014c,
title = {An Information Retrieval Ontology for Information Retrieval Nanopublications},
author = {Aldo Lipani and Florina Piroi and Linda Andersson and Allan Hanbury},
doi = {10.1007/978-3-319-11382-1_5},
year = {2014},
date = {2014-01-01},
booktitle = {Proc.~of CLEF},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}