Aldo Lipani

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

SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0-0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63-0.69) and low uncertainty (s.d.<0.76gCkg-1 with RS, and <1.25gCkg-1 without RS). These outputs allowed depicting a time variation of SOC at 1arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance.

The Impact of Fixed-Cost Pooling Strategies on Test Collection Bias

In Information Retrieval, test collections are usually built using the pooling method. Many pooling strategies have been developed for the pooling method. Herein, we address the question of identifying the best pooling strategy when evaluating systems using precision-oriented measures in presence of budget constraints on the number of documents to be evaluated. As a quality measurement we use the bias introduced by the pooling strategy, measured both in terms of Mean Absolute Error of the scores and in terms of ranking errors. Based on experiments on 15 test collections, we conclude that, for precision-oriented measures, the best strategies are based on Rank-Biased Precision (RBP). These results can inform collection builders because they suggest that, under fixed assessment budget constraints, RBP-based sampling produces less biased pools than other alternatives.

The Curious Incidence of Bias Corrections in the Pool

Recently, it has been discovered that it is possible to mitigate the Pool Bias of Precision at cut-off (P@n) when used with the fixed-depth pooling strategy, by measuring the effect of the tested run against the pooled runs. In this paper we extend this analysis and test the existing methods on different pooling strategies, simulated on a selection of 12 TREC test collections. We observe how the different methodologies to correct the pool bias behave, and provide guidelines about which pooling strategy should be chosen.

An Information Retrieval Ontology for Information Retrieval Nanopublications

Retrieval experiments produce plenty of data, like various experiment settings and experimental results, that are usually not all included in the published articles. Even if they are mentioned, they are not easily machine-readable. We propose the use of IR nanopublications to describe in a formal language such information. Furthermore, to support the unambiguous description of IR domain aspects, we present a preliminary IR ontology. The use of the IR nanopublications will facilitate the assessment and comparison of IR systems and enhance the degree of reproducibility and reliability of IR research progress.

Fixed-Cost Pooling Strategies Based on IR Evaluation Measures

Recent studies have reconsidered the way we operationalise the pooling method, by considering the practical limitations often encountered by test collection builders. The biggest constraint is often the budget available for relevance assessments and the question is how best – in terms of the lowest pool bias – to select the documents to be assessed given a fixed budget. Here, we explore a series of 3 new pooling strategies introduced in this paper against 3 existing ones and a baseline. We show that there are significant differences depending on the evaluation measure ultimately used to assess the runs. We conclude that adaptive strategies are always best, but in their absence, for top-heavy evaluation measures we can continue to use the baseline, while for P@100 we should use any of the other non-adaptive strategies.

Verboseness Fission for BM25 Document Length Normalization

BM25 is probably the most well known term weighting model in Information Retrieval. It has, depending on the formula variant at hand, 2 or 3 parameters (k1, b, and k3). This paper addresses b - the document length normalization parameter. Based on the observation that the two cases previously discussed for length normalization (multi-topicality and verboseness) are actually three: multi-topicality, verboseness with word repetition (repetitiveness) and verboseness with synonyms, we propose and test a new length normalization method that removes the need for a b parameter in BM25. Testing the new method on a set of purposefully varied test collections, we observe that we can obtain results statistically indistinguishable from the optimal results, therefore removing the need for ground-truth based optimization.

Fixed budget pooling strategies based on fusion methods

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.

An Initial Analytical Exploration of Retrievability

We approach the problem of retrievability from an analytical perspective, starting with modeling conjunctive and disjunctive queries in a boolean model. We show that this represents an upper bound on retrievability for all other best match algorithms. We follow this with an observation of imbalance in the distribution of retrievability, using the Gini coefficient. Simulation-based experiments show the behavior of the Gini coefficient for retrievability under different types and lengths of queries, as well as different assumptions about the document length distribution in a collection.

DASyR(IR) - document analysis system for systematic reviews (in Information Retrieval)

Creating systematic reviews is a painstaking task undertaken especially in domains where experimental results are the primary method to knowledge creation. For the review authors, analysing documents to extract relevant data is a demanding activity. To support the creation of systematic reviews, we have created DASyR-a semi-automatic document analysis system. DASyR is our solution to annotating published papers for the purpose of ontology population. For domains where dictionaries are not existing or inadequate, DASyR relies on a semi-automatic annotation bootstrapping method based on positional Random Indexing, followed by traditional Machine Learning algorithms to extend the annotation set. We provide an example of the method application to a subdomain of Computer Science, the Information Retrieval evaluation domain. The reliance of this domain on large scale experimental studies makes it a perfect domain to test on. We show the utility of DASyR through experimental results for different parameter values for the bootstrap procedure, evaluated in terms of annotator agreement, error rate, precision and recall.

Visual Pool: A Tool to Visualize and Interact with the Pooling Method

Every year more than 25 test collections are built among the main Information Retrieval (IR) evaluation campaigns. They are extremely important in IR because they become the evaluation praxis for the forthcoming years. Test collections are built mostly using the pooling method. The main advantage of this method is that it drastically reduces the number of documents to be judged. It does so at the cost of introducing biases, which are sometimes aggravated by non optimal configuration. In this paper we develop a novel visualization technique for the pooling method, and integrate it in a demo application named Visual Pool. This demo application enables the user to interact with the pooling method with ease, and develops visual hints in order to analyze existing test collections, and build better ones.