Evaluation of information retrieval systems using structural equation modeling

The interpretation of the experimental data collected by testing systems across input datasets and model parameters is of strategic importance for system design and implementation. In particular, finding relationships between variables and detecting the latent variables affecting retrieval performance can provide designers, engineers and experimenters with useful if not necessary information about how a system is performing. This paper discusses the use of Structural Equation Modeling (SEM) in providing an in-depth explanation of evaluation results and an explanation of failures and successes of a system; in particular, we focus on the case of evaluation of Information Retrieval systems

Querylog-based assessment of retrievability bias in a large newspaper corpus

Bias in the retrieval of documents can directly influence the information access of a digital library. In the worst case, systematic favoritism for a certain type of document can render other parts of the collection invisible to users. This potential bias can be evaluated by measuring the retrievability for all documents in a collection. Previous evaluations have been performed on TREC collections using simulated query sets. The question remains, however, how representative this approach is of more realistic settings. To address this question, we investigate the effectiveness of the retrievability measure using a large digitized newspaper corpus, featuring two characteristics that distinguishes our experiments from previous studies: (1) compared to TREC collections, our collection contains noise originating from OCR processing, historical spelling and use of language; and (2) instead of simula

PRES: A score metric for evaluating recall-oriented information retrieval applications

Information retrieval (IR) evaluation scores are generally designed to measure the effectiveness with which relevant documents are identified and retrieved. Many scores have been proposed for this purpose over the years. These have primarily focused on aspects of precision and recall, and while these are often discussed with equal importance, in practice most attention has been given to precision focused metrics. Even for recalloriented IR tasks of growing importance, such as patent retrieval, these precision based scores remain the primary evaluation measures. Our study examines different evaluation measures for a recall-oriented patent retrieval task and demonstrates the limitations of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and the user’s search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the user’s expected search effort

Preliminary study of technical terminology for the retrieval of scientific book metadata records

Books only represented by brief metadata (book records) are particularly hard to retrieve. One way of improving their retrieval is by extracting retrieval enhancing features from them. This work focusses on scientific (physics) book records. We ask if their technical terminology can be used as a retrieval enhancing feature. A study of 18,443 book records shows a strong correlation between their technical terminology and their likelihood of relevance. Using this finding for retrieval yields >+5% precision and recall gains

Assessing the impact of OCR quality on downstream NLP tasks

A growing volume of heritage data is being digitized and made available as text via optical character recognition (OCR). Scholars and libraries are increasingly using OCR-generated text for retrieval and analysis. However, the process of creating text through OCR introduces varying degrees of error to the text. The impact of these errors on natural language processing (NLP) tasks has only been partially studied. We perform a series of extrinsic assessment tasks — sentence segmentation, named entity recognition, dependency parsing, information retrieval, topic modelling and neural language model fine-tuning — using popular, out-of-the-box tools in order to quantify the impact of OCR quality on these tasks. We find a consistent impact resulting from OCR errors on our downstream tasks with some tasks more irredeemably harmed by OCR errors. Based on these results, we offer some preliminary guidelines for working with text produced through OCR

A topical approach to retrievability bias estimation

Retrievability is an independent evaluation measure that offers insights to an aspect of retrieval systems that performance and efficiency measures do not. Retrievability is often used to calculate the retrievability bias, an indication of how accessible a system makes all the documents in a collection. Generally, computing the retrievability bias of a system requires a colossal number of queries to be issued for the system to gain an accurate estimate of the bias. However, it is often the case that the accuracy of the estimate is not of importance, but the relationship between the estimate of bias and performance when tuning a systems parameters. As such, reaching a stable estimation of bias for the system is more important than getting very accurate retrievability scores for individual documents. This work explores the idea of using topical subsets of the collection for query generation and bias estimation to form a local estimate of bias which correlates with the global estimate of retrievability bias. By using topical subsets, it would be possible to reduce the volume of queries required to reach an accurate estimate of retrievability bias, reducing the time and resources required to perform a retrievability analysis. Findings suggest that this is a viable approach to estimating retrievability bias and that the number of queries required can be reduced to less than a quarter of what was previously thought necessary

The relationship between retrievability bias and retrieval performance

A long standing problem in the domain of Information Retrieval (IR) has been the influence of biases within an IR system on the ranked results presented to a user. Retrievability is an IR evaluation measure which provides a means to assess the level of bias present in a system by evaluating how \emph{easily} documents in the collection can be found by the IR system in place. Retrievability is intrinsically related to retrieval performance because a document needs to be retrieved before it can be judged relevant. It is therefore reasonable to expect that lowering the level of bias present within a system could lead to improvements in retrieval performance. In this thesis, we undertake an investigation of the nature of the relationship between classical retrieval performance and retrievability bias. We explore the interplay between the two as we alter different aspects of the IR system in an attempt to investigate the \emph{Fairness Hypothesis}: that a system which is fairer (i.e. exerts the least amount of retrievability bias), performs better. To investigate the relationship between retrievability bias and retrieval performance we utilise a set of 6 standard TREC collections (3 news and 3 web) and a suite of standard retrieval models. We investigate this relationship by looking at four main aspects of the retrieval process using this set of TREC collections to also explore how generalisable the findings are. We begin by investigating how the retrieval model used relates to both bias and performance by issuing a large set of queries to a set of common retrieval models. We find a general trend where using a retrieval model that is evaluated to be more \emph{fair} (i.e. less biased) leads to improved performance over less fair systems. Hinting that providing documents with a more equal opportunity for access can lead to better retrieval performance. Following on from our first study, we investigate how bias and performance are affected by tuning length normalisation of several parameterised retrieval models. We explore the space of the length normalisation parameters of BM25, PL2 and Language Modelling. We find that tuning these parameters often leads to a trade off between performance and bias such that minimising bias will often not equate to maximising performance when traditional TREC performance measures are used. However, we find that measures which account for document length and users stopping strategies tend to evaluate the least biased settings to also be the maximum (or near maximum) performing parameter, indicating that the Fairness Hypothesis holds. Following this, we investigate the impact that query length has on retrievability bias. We issue various automatically generated query sets to the system to see if longer or shorter queries tend to influence the level of bias associated with the system. We find that longer queries tend to reduce bias, possibly due to the fact that longer queries will often lead to more documents being retrieved, but the reductions in bias are in diminishing returns. Our studies show that after issuing two terms, each additional term reduces bias by significantly less. Finally, we build on our work by employing some fielded retrieval models. We look at typical fielding, where the field relevance scores are computed individually then combined, and compare it with an enhanced version of fielding, where fields are weighted and combined then scored. We see that there are inherent biases against particular documents in the former model, especially in cases where a field is empty and as such see the latter tends to both perform better and also lower bias when compared with the former. In this thesis, we have examined several different ways in which performance and bias can be related. We conclude that while the Fairness Hypothesis has its merits, it is not a universally applicable idea. We further add to this by noting that the method used to compute bias does not distinguish between positive and negative biases and this influences our results. We do however support the idea that reducing the bias of a system by eliminating biases that are known to be negative should result in improvements in system performance