Offline evaluation of search systems depends on test collections. These benchmarks provide the researchers with a corpus of documents, topics and relevance judgements indicating which documents are relevant for each topic. While test collections are an integral part of Information Retrieval (IR) research, their creation involves significant efforts in manual annotation. Large language models (LLMs) are gaining much attention as tools for automatic relevance assessment. Recent research has shown that LLM-based assessments yield high systems ranking correlation with human-made judgements. These correlations are helpful in large-scale experiments but less informative if we want to focus on top-performing systems. Moreover, these correlations ignore whether and how LLM-based judgements impact the statistically significant differences among systems with respect to human assessments. In this work, we look at how LLM-generated judgements preserve ranking differences among top-performing systems and also how they preserve pairwise significance evaluation as human judgements. Our results show that LLM-based judgements are unfair at ranking top-performing systems. Moreover, we observe an exceedingly high rate of false positives regarding statistical differences.

People tend to consider social platforms as convenient media for expressing their concerns and emotional struggles. With their widespread use, researchers could access and analyze user-generated content related to mental states. Computational models that exploit that data show promising results in detecting at-risk users based on engineered features or deep learning models. However, recent works revealed that these approaches have a limited capacity for generalization and interpretation when considering clinical settings. Grounding the models' decisions on clinical and recognized symptoms can help to overcome these limitations. In this paper, we introduce BDI-Sen, a symptom-annotated sentence dataset for depressive disorder. BDI-Sen covers all the symptoms present in the Beck Depression Inventory-II (BDI-II), a reliable questionnaire used for detecting and measuring depression. The annotations in the collection reflect whether a statement about the specific symptom is informative (i.e., exposes traces about the individual's state regarding that symptom). We thoroughly analyze this resource and explore linguistic style, emotional attribution, and other psycholinguistic markers. Additionally, we conduct a series of experiments investigating the utility of BDI-Sen for various tasks, including the detection and severity classification of symptoms. We also examine their generalization when considering symptoms from other mental diseases. BDI-Sen may aid the development of future models that consider trustworthy and valuable depression markers.

Depression is one of the most common mental health illnesses. The biggest obstacle lies in an efficient and early detection of the disorder. Self-report questionnaires are the instruments used by medical experts to elaborate a diagnosis. These questionnaires were designed by analyzing different depressive symptoms. However, factors such as social stigmas negatively affect the success of traditional methods. This paper presents a novel approach for automatically estimating the degree of depression in social media users. In this regard, we addressed the task Measuring the Severity of the Signs of Depression of eRisk 2020, an initiative in the CLEF Conference. We aimed to explore neural language models to exploit different aspects of the subject's writings depending on the symptom to capture. We devised two distinct methods based on the symptoms' sensitivity in terms of willingness on commenting about them publicly. The first exploits users' general language based on their publications. The second seeks more direct evidence from publications that specifically mention the symptoms concerns. Both methods automatically estimate the Beck Depression Inventory (BDI-II) total score. For evaluating our proposals, we used benchmark Reddit data for depression severity estimation. Our findings showed that approaches based on neural language models are a feasible alternative for estimating depression rating scales, even when small amounts of training data are available.

In the field of Information Retrieval, word embedding models have shown to be effective in several tasks. In this paper, we show how one of these neural embedding techniques can be adapted to the recommendation task. This adaptation only makes use of collaborative filtering information, and the results show that it is able to produce effective recommendations efficiently.

Statistical significance tests can provide evidence that the observed difference in performance between 2 methods is not due to chance. In information retrieval (IR), some studies have examined the validity and suitability of such tests for comparing search systems. We argue here that current methods for assessing the reliability of statistical tests suffer from some methodological weaknesses, and we propose a novel way to study significance tests for retrieval evaluation. Using Score Distributions, we model the output of multiple search systems, produce simulated search results from such models, and compare them using various significance tests. A key strength of this approach is that we assess statistical tests under perfect knowledge about the truth or falseness of the null hypothesis. This new method for studying the power of significance tests in IR evaluation is formal and innovative. Following this type of analysis, we found that both the sign test and Wilcoxon signed test have more power than the permutation test and the t‐test. The sign test and Wilcoxon signed test also have good behavior in terms of type I errors. The bootstrap test shows few type I errors, but it has less power than the other methods tested.

In information retrieval evaluation, pooling is a well‐known technique to extract a sample of documents to be assessed for relevance. Given the pooled documents, a number of studies have proposed different prioritization methods to adjudicate documents for judgment. These methods follow different strategies to reduce the assessment effort. However, there is no clear guidance on how many relevance judgments are required for creating a reliable test collection. In this article we investigate and further develop methods to determine when to stop making relevance judgments. We propose a highly diversified set of stopping methods and provide a comprehensive analysis of the usefulness of the resulting test collections. Some of the stopping methods introduced here combine innovative estimates of recall with time series models used in Financial Trading. Experimental results on several representative collections show that some stopping methods can reduce up to 95% of the assessment effort and still produce a robust test collection. We demonstrate that the reduced set of judgments can be reliably employed to compare search systems using disparate effectiveness metrics such as Average Precision, NDCG, P@100, and Rank Biased Precision. With all these measures, the correlations found between full pool rankings and reduced pool rankings is very high.

Given the diversity of recommendation algorithms, choosing one technique is becoming increasingly difficult. In this paper, we explore methods for combining multiple recommendation approaches. We studied rank aggregation methods that have been proposed for the metasearch task (i.e., fusing the outputs of different search engines) but have never been applied to merge top-N recommender systems. These methods require no training data nor parameter tuning. We analysed two families of methods: voting-based and score-based approaches. These rank aggregation techniques yield significant improvements over state-of-the-art top-N recommenders. In particular, score-based methods yielded good results; however, some voting techniques were also competitive without using score information, which may be unavailable in some recommendation scenarios. The studied methods not only improve the state of the art of recommendation algorithms but they are also simple and efficient.

Language Models constitute an effective framework for text retrieval tasks. Recently, it has been extended to various collaborative filtering tasks. In particular, relevance-based language models can be used for generating highly accurate recommendations using a memory-based approach. On the other hand, the query likelihood model has proven to be a successful strategy for neighbourhood computation. Since relevance-based language models rely on user neighbourhoods for producing recommendations, we propose to use the query likelihood model for computing those neighbourhoods instead of cosine similarity. The combination of both techniques results in a formal probabilistic recommender system which has not been used before in collaborative filtering. A thorough evaluation on three datasets shows that the query likelihood model provides better results than cosine similarity. To understand this improvement, we devise two properties that a good neighbourhood algorithm should satisfy. Our axiomatic analysis shows that the query likelihood model always enforces those constraints while cosine similarity does not.

Probabilistic modelling of recommender systems naturally introduces the concept of prior probability into the recommendation task. Relevance-Based Language Models, a principled probabilistic query expansion technique in Information Retrieval, has been recently adapted to the item recommendation task with success. In this paper, we study the effect of the item and user prior probabilities under that framework. We adapt two priors from the document retrieval field and then we propose other two new probabilistic priors. Evidence gathered from experimentation indicates that a linear prior for the neighbour and a probabilistic prior based on Dirichlet smoothing for the items improve the quality of the item recommendation ranking.

Novelty detection is a difficult task, particularly at sentence level. Most of the approaches proposed in the past consist of re-ordering all sentences following their novelty scores. However, this re-ordering has usually little value. In fact, a naive baseline with no novelty detection capabilities yields often better performance than any state-of-the-art novelty detection mechanism. We argue here that this is because current methods initiate too early the novelty detection process. When few sentences have been seen, it is unlikely that the user is negatively affected by redundancy. Therefore, re-ordering the first sentences may be harmful in terms of performance. We propose here a query-dependent method based on cluster analysis to determine where we must start filtering redundancy.

In the last years Blog Search has been a new exciting task in Information Retrieval. The presence of user generated information with valuable opinions makes this field of huge interest. In this poster we use part of this information, the readers' comments, to improve the quality of post snippets with the objective of enhancing the user access to the relevant posts in a result list. We propose a simple method for snippet generation based on sentence selection, using the comments to guide the selection process. We evaluated our approach with standard TREC methodology in the Blogs06 collection showing significant improvements up to 32% in terms of MAP over the baseline.

Information retrieval (IR) systems typically compress their indexes in order to increase their efficiency. Static pruning is a form of lossy data compression: it removes from the index, data that is estimated to be the least important to retrieval performance, according to some criterion. Generally, pruning criteria are derived from term weighting functions, which assign weights to terms according to their contribution to a document's contents. Usually, document-term occurrences that are assigned a low weight are ruled out from the index. The main assumption is that those entries contribute little to the document content. We present a novel pruning technique that is based on a probabilistic model of IR. We employ the Probability Ranking Principle as a decision criterion over which posting list entries are to be pruned. The proposed approach requires the estimation of three probabilities, combining them in such a way that we gather all the necessary information to apply the aforementioned criterion. We evaluate our proposed pruning technique on five TREC collections and various retrieval tasks, and show that in almost every situation it outperforms the state of the art in index pruning. The main contribution of this work is proposing a pruning technique that stems directly from the same source as probabilistic retrieval models, and hence is independent of the final model used for retrieval.

The inclusion of document length factors has been a major topic in the development of retrieval models. We believe that current models can be further improved by more refined estimations of the document's scope. In this poster we present a new document length prior that uses the size of the compressed document. This new prior is introduced in the context of Language Modeling with Dirichlet smoothing. The evaluation performed on several collections shows significant improvements in effectiveness.

We present an approach to document clustering based on winnowing fingerprints that achieved good values of effectiveness with considerable save in memory space and computation time.

This paper revisits the static term-based pruning technique presented in Carmel et al., SIGIR 2001 for ad-hoc retrieval, addressing different issues concerning its algorithmic design not yet taken into account. Although the original technique is able to retain precision when a considerable part of the inverted file is removed, we show that it is possible to improve precision in some scenarios if some key design features are properly selected.

In this poster, we analyze recent work in the document identifiers reassignment problem. After that, we present a formalization of a simple case of the problem as a PSP (Pattern Sequencing Problem). This may facilitate future work as it opens a new research line to solve the general problem.

Abstract We propose a novel approach to incorporate term similarity and inverse document frequency into a logical model of information retrieval. The ability of the logic to handle expressive representations along with the use of such classical notions are promising characteristics for IR systems. The approach proposed here has been efficiently implemented and experiments against test collections are presented.

Relevance feedback is an appreciated process to produce increasingly better retrieval. Usually, positive feedback plays a fundamental role in the feedback process whereas the role of negative feedback is limited. We think that negative feedback is a promising precision oriented mechanism and we propose a logical framework in which positive and negative feedback are homogeneously modeled. Evaluation results against small test collections are provided.

A logical model for information retrieval is presented. Documents and queries are represented by propositional formulas and we apply techniques of the field of belief revision to get a measure of similarity between documents and queries. The model is further extended to deal with retrieval situations. Generality and expressiveness are fundamental properties of the model. We stress the advantages of these notions in realistic scenarios. We have paid great attention to the complexity of the objective tasks and present algorithms that run in polynomial time for both the computation of the similarity between documents and queries and the inclusion of retrieval situations in the model. The model can constitute the basis of a realistic information retrieval system.

Deals with the implementation of a logical model of information retrieval. Specifically, we present algorithms for document ranking within the belief revision framework. Therefore, the logical model that stands on the basis of our proposal can be efficiently implemented within realistic systems. Besides the inherent advantages introduced by logic, the expressiveness is extended with respect to classical systems because documents are represented as unrestricted propositional formulas. As well as representing classical vectors, the model can deal with partial descriptions of documents. Scenarios that can benefit from these more expressive representations are discussed.

This paper claims that Belief Revision can be seen as a theoretical framework for document ranking in Extended Boolean Models.For a model of Information Retrieval based on propositional logic, we propose a similarity measure which is equivalent to a P-Norm case.Therefore it shares the P-Norm good properties and behaviour.Besides, it is theoretically ensured that this measure follows the notion of proximity b e t ween the documents and the query.The logical model can naturally deal with incomplete descriptions of documents and the similarity v alues are also obtained for this case.