This work presents a method for the measurement of the accuracy of evidential artifact extraction and categorization tasks in digital forensic investigations. Instead of focusing on the measurement of accuracy and errors in the functions of digital forensic tools, this work proposes the application of information retrieval measurement techniques that allow the incorporation of errors introduced by tools and analysis processes. This method uses a ‘gold standard’ that is the collection of evidential objects determined by a digital investigator from suspect data with an unknown ground truth. This work proposes that the accuracy of tools and investigation processes can be evaluated compared to the derived gold standard using common precision and recall values. Two example case studies are presented showing the measurement of the accuracy of automated analysis tools as compared to an in-depth analysis by an expert. It is shown that such measurement can allow investigators to determine changes in accuracy of their processes over time, and determine if such a change is caused by their tools or knowledge.
Information Retrieval (IR) is one area where accuracy measurement is paramount. Much work has been done in the area of IR, and IR accuracy measurement techniques have previously been applied to forensic text sting searching @cite_19 , document classification @cite_4 , and even fragmented document analysis in digital forensics @cite_23 . The focus, however, has been on the accuracy measurement of particular techniques or tools within the digital examination process, and not for the examination process itself.
@cite_19 @cite_4 @cite_23
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Ranking information retrieval (IR) systems with respect to their effectiveness is a crucial operation during IR evaluation, as well as during data fusion. This article offers a novel method of approaching the system-ranking problem, based on the widely studied idea of polyrepresentation. The principle of polyrepresentation suggests that a single information need can be represented by many query articulations–what we call query aspects. By skimming the top k (where k is small) documents retrieved by a single system for multiple query aspects, we collect a set of documents that are likely to be relevant to a given test topic. Labeling these skimmed documents as putatively relevant lets us build pseudorelevance judgments without undue human intervention. We report experiments where using these pseudorelevance judgments delivers a rank ordering of IR systems that correlates highly with rankings based on human relevance judgments. © 2010 Wiley Periodicals, Inc. Familiar evaluation methodologies for information retrieval (IR) are not well suited to the task of comparing systems in many real settings. These systems and evaluation methods must support contextual, interactive retrieval over changing, heterogeneous data collections, including private and confidential information.We have implemented a comparison tool which can be inserted into the natural IR process. It provides a familiar search interface, presents a small number of result sets in side-by-side panels, elicits searcher judgments, and logs interaction events. The tool permits study of real information needs as they occur, uses the documents actually available at the time of the search, and records judgments taking into account the instantaneous needs of the searcher.We have validated our proposed evaluation approach and explored potential biases by comparing different whole-of-Web search facilities using a Web-based version of the tool. In four experiments, one with supplied queries in the laboratory and three with real queries in the workplace, subjects showed no discernable left-right bias and were able to reliably distinguish between high- and low-quality result sets. We found that judgments were strongly predicted by simple implicit measures.Following validation we undertook a case study comparing two leading whole-of-Web search engines. The approach is now being used in several ongoing investigations. The poster discusses the characteristics needed in an information retrieval (IR) test collection to facilitate the evaluation of integrated search, i.e. search across a range of different sources but with one search box and one ranked result list, and describes and analyses a new test collection constructed for this purpose. The test collection consists of approx. 18,000 monographic records, 160,000 papers and journal articles in PDF and 275,000 abstracts with a varied set of metadata and vocabularies from the physics domain, 65 topics based on real work tasks and corresponding graded relevance assessments. The test collection may be used for systems- as well as user-oriented evaluation. Modern large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation. In order to develop IR techniques in this direction, it is necessary to develop evaluation approaches and methods that credit IR methods for their ability to retrieve highly relevant documents. This can be done by extending traditional evaluation methods, that is, recall and precision based on binary relevance judgments, to graded relevance judgments. Alternatively, novel measures based on graded relevance judgments may be developed. This article proposes several novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. The first one accumulates the relevance scores of retrieved documents along the ranked result list. The second one is similar but applies a discount factor to the relevance scores in order to devaluate late-retrieved documents. The third one computes the relative-to-the-ideal performance of IR techniques, based on the cumulative gain they are able to yield. These novel measures are defined and discussed and their use is demonstrated in a case study using TREC data: sample system run results for 20 queries in TREC-7. As a relevance base we used novel graded relevance judgments on a four-point scale. The test results indicate that the proposed measures credit IR methods for their ability to retrieve highly relevant documents and allow testing of statistical significance of effectiveness differences. The graphs based on the measures also provide insight into the performance IR techniques and allow interpretation, for example, from the user point of view. central problem of information retrieval (IR) is to determine which documents are relevant and which are not to the user information need. This problem is practically handled by a ranking function which defines an ordering among documents according to their degree of relevance to the user query. This paper discusses work on using machine learning to automatically generate an effective ranking function for IR. This task is referred to as "learning to rank for IR" in the field. In this paper, a learning method, RankGP, is presented to address this task. RankGP employs genetic programming to learn a ranking function by combining various types of evidences in IR, including content features, structure features, and query-independent features. The proposed method is evaluated using the LETOR benchmark datasets and found to be competitive with Ranking SVM and RankBoost. This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in modem large IR environments. The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) two novel measures computing the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. We then demonstrate the use of these evaluation methods in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system (In- Query I) in a text database consisting of newspaper articles. The results indicate that the tested strong query structures are most effective in retrieving highly relevant documents. The differences between the query types are practically essential and statistically significant. More generally, the novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods. Mobile Information Retrieval (Mobile IR) is a relatively recent branch of Information Retrieval (IR) that is concerned with enabling users to carry out, using a mobile device, all the classical IR operations that they were used to carry out on a desktop. This includes finding content available on local repositories or on the web in response to a user query, interacting with the system in an explicit or implicit way, reformulate the query and or visualise the content of the retrieved documents, as well as providing relevance judgments to improve the retrieval process. This book is structured as follows. Chapter 2 provides a very brief overview of IR and of Mobile IR, briefly outlining what in Mobile IR is different from IR. Chapter 3 provides the foundations of Mobile IR, looking at the characteristics of mobile devices and what they bring to IR, but also looking at how the concept of relevance changed from standard IR to Mobile IR. Chapter 4 presents an overview of the document collections that are searchable by a Mobile IR system, and that are somehow different from classical IR ones; available for experimentation, including collections of data that have become complementary to Mobile IR. Similarly, Chapter 5 reviews mobile information needs studies and users log analysis. Chapter 6 reviews studies aimed at adapting and improving the users interface to the needs of Mobile IR. Chapter 7, instead, reviews work on context awareness, which studies the many aspects of the user context that Mobile IR employs. Chapter 8 reviews some of evaluation work done in Mobile IR, highlighting the distinctions with classical IR from the perspectives of two main IR evaluation methodologies: users studies and test collections. Finally, Chapter 9 reports the conclusions of this review, highlighting briefly some trends in Mobile IR that we believe will drive research in the next few years. Given the phenomenal growth in the variety and quantity of data available to users through electronic media, there is a great demand for efficient and effective ways to organize and search through all this information. Besides speech, our principal means of communication is through visual media, and in particular, through documents. In this paper, we provide an update on Doermann's comprehensive survey (1998) of research results in the broad area of document-based information retrieval. The scope of this survey is also somewhat broader, and there is a greater emphasis on relating document image analysis methods to conventional IR methods. Documents are available in a wide variety of formats. Technical papers are often available as ASCII files of clean, correct, text. Other documents may only be available as hardcopies. These documents have to be scanned and stored as images so that they may be processed by a computer. The textual content of these documents may also be extracted and recognized using OCR methods. Our survey covers the broad spectrum of methods that are required to handle different formats like text and images. The core of the paper focuses on methods that manipulate document images directly, and perform various information processing tasks such as retrieval, categorization, and summarization, without attempting to completely recognize the textual content of the document. We start, however, with a brief overview of traditional IR techniques that operate on clean text. We also discuss research dealing with text that is generated by running OCR on document images. Finally, we also briefly touch on the related problem of content-based image retrieval. In ad hoc information retrieval (IR), some information need (e.g., find the advantages and disadvantages of smoking) requires the explicit identification of information related to the discourse type (e.g., advantages disadvantages) as well as to the topic (e.g., smoking). Such information need is not uncommon and may not be satisfied by using conventional retrieval methods. We extend existing retrieval models by adding a re-ranking strategy based on a novel graph-based retrieval model using document contexts that are called information units (IU). For evaluation, we focused on a discourse type that appeared in a subset of TREC topics where the retrieval effectiveness achieved by our conventional retrieval models for those topics was low. We showed that our approach is able to enhance the retrieval effectiveness for the selected TREC topics. This shows that our preliminary investigation is promising and deserves further investigation. This paper presents the experiments and results of DCU in CLEF-IP 2009. Our work applied standard information retrieval (IR) techniques to patent search. Different experiments tested various methods for the patent retrieval, including query formulation, structured index, weighted fields, document filtering, and blind relevance feedback. Some methods did not show expected good retrieval effectiveness such as blind relevance feedback, other experiments showed acceptable performance. Query formulation was the key to achieving better retrieval effectiveness, and this was performed through assigning higher weights to certain document fields. Further experiments showed that for longer queries, better results are achieved but at the expense of additional computations. For the best runs, the retrieval effectiveness is still lower than for IR applications for other domains, illustrating the difficulty of patent search. The official results have shown that among fifteen participants we achieved the seventh and the fourth ranks from the mean average precision (MAP) and recall point of view, respectively. Technology advances in the areas of image processing (IP) and information retrieval (IR) have evolved separately for a long time. However, successful content-based image retrieval systems require the integration of the two. There is an urgent need to develop integration mechanisms to link the image retrieval model to text retrieval model, such that the well established text retrieval techniques can be utilized. Approaches of converting image feature vectors (IF domain) to weighted-term vectors (IR domain) are proposed in this paper. Furthermore, the relevance feedback technique from the IR domain is used in content-based image retrieval to demonstrate the effectiveness of this conversion. Experimental results show that the image retrieval precision increases considerably by using the proposed integration approach. While information retrieval(IR) and databases(DB) have been developed independently, there have been emerging requirements that both data management and efficient text retrieval should be supported simultaneously in an information system such as health care systems, bulletin boards, XML data management, and digital libraries. Recently DB-IR integration issue has been budded in the research field. The great divide between DB and IR has caused different manners in index maintenance for newly arriving documents. While DB has extended its SQL layer to cope with text fields due to lack of intact mechanism to build IR-like index, IR usually treats a block of new documents as a logical unit of index maintenance since it has no concept of integrity constraint. However, towards DB-IR integration, a transaction on adding or updating a document should include maintenance of the postings lists accompanied by the document - hence per-document basis transactional index maintenance. In this paper, performance of a few strategies for per-document basis transaction for inserting documents -- direct index update, stand-alone auxiliary index and pulsing auxiliary index - will be evaluated. The result tested on the KRISTAL-IRMS shows that the pulsing auxiliary strategy, where long postings lists in the auxiliary index are in-place updated to the main index whereas short lists are directly updated in the auxiliary index, can be a challenging candidate for text field indexing in DB-IR integration. In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task. Ranking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the extent to which the system provides "the right information, to the right people, in the right way, at the right time." However, people in current IR systems are not only the ones issuing search queries, but increasingly they are also the ones being searched. This raises several new problems in IR that have been addressed in recent research, particularly with respect to fairness non-discrimination, accountability, and transparency. This is a summary of some these initial developments. In the field of information retrieval (IR), researchers and practitioners are often faced with a demand for valid approaches to evaluate the performance of retrieval systems. The Cranfield experiment paradigm has been dominant for the in-vitro evaluation of IR systems. Alternative to this paradigm, laboratory-based user studies have been widely used to evaluate interactive information retrieval (IIR) systems, and at the same time investigate users' information searching behaviours. Major drawbacks of laboratory-based user studies for evaluating IIR systems include the high monetary and temporal costs involved in setting up and running those experiments, the lack of heterogeneity amongst the user population and the limited scale of the experiments, which usually involve a relatively restricted set of users. In this paper, we propose an alternative experimental methodology to laboratory-based user studies. Our novel experimental methodology uses a crowdsourcing platform as a means of engaging study participants. Through crowdsourcing, our experimental methodology can capture user interactions and searching behaviours at a lower cost, with more data, and within a shorter period than traditional laboratory-based user studies, and therefore can be used to assess the performances of IIR systems. In this article, we show the characteristic differences of our approach with respect to traditional IIR experimental and evaluation procedures. We also perform a use case study comparing crowdsourcing-based evaluation with laboratory-based evaluation of IIR systems, which can serve as a tutorial for setting up crowdsourcing-based IIR evaluations. In this paper we present some new methods of ranking information retrieval systems without relevance judgement. The common ground of these methods is using a measure we called reference count. An extensive experimentation was conducted to evaluate the effectiveness of the proposed methods using various different standards Information Retrieval evaluation measures for the ranking, like average precision, R-precision, and precision and different document levels. We also compared the effectiveness of the proposed methods with the method proposed by The experimental results showed that the proposed methods are effective, and in many cases are more effective than Soboroff at al.'s method. Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank. Search queries are often ambiguous and or underspecified. To accomodate different user needs, search result diversification has received attention in the past few years. Accordingly, several new metrics for evaluating diversification have been proposed, but their properties are little understood. We compare the properties of existing metrics given the premises that (1) queries may have multiple intents; (2) the likelihood of each intent given a query is available; and (3) graded relevance assessments are available for each intent. We compare a wide range of traditional and diversified IR metrics after adding graded relevance assessments to the TREC 2009 Web track diversity task test collection which originally had binary relevance assessments. Our primary criterion is discriminative power, which represents the reliability of a metric in an experiment. Our results show that diversified IR experiments with a given number of topics can be as reliable as traditional IR experiments with the same number of topics, provided that the right metrics are used. Moreover, we compare the intuitiveness of diversified IR metrics by closely examining the actual ranked lists from TREC. We show that a family of metrics called D#-measures have several advantages over other metrics such as α-nDCG and Intent-Aware metrics. Abstract With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user’s initial query is reformulated by adding additional meaningful terms with similar significance. QE – as part of information retrieval (IR) – has long attracted researchers’ attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications – bringing out similarities and differences. To create a new IR test collection at minimal cost, we must carefully select which documents merit human relevance judgments. Shared task campaigns such as NIST TREC determine this by pooling search results from many participating systems (and often interactive runs as well), thereby identifying the most likely relevant documents in a given collection. While effective, it would be preferable to be able to build a new test collection without needing to run an entire shared task. Toward this end, we investigate multiple active learning (AL) strategies which, without reliance on system rankings: 1) select which documents human assessors should judge; and 2) automatically classify the relevance of remaining unjudged documents. Because scarcity of relevant documents tends to yield highly imbalanced training data for model estimation, we investigate sampling strategies to mitigate class imbalance. We report experiments on four TREC collections with varying scarcity of relevant documents, reporting labeling accuracy achieved, as well as rank correlation when evaluating participant systems using these labels vs. NIST judgments. Results demonstrate the effectiveness of our approach, coupled with further analysis showing how varying relevance scarcity, within and across collections, impacts findings.