Video test collection with graded relevance assessments

Relevance is a complex, but core, concept within the field of Information Retrieval. In order to allow system comparisons the many factors that influence relevance are often discarded to allow abstraction to a single score relating to relevance. This means that a great wealth of information is often discarded. In this paper we outline the creation of a video test collection with graded relevance assessments, to the best of our knowledge the first example of such a test collection for video retrieval. To directly address the shortcoming above we also gathered behavioural and perceptual data from assessors during the assessment process. All of this information along with judgements are available for download. Our intention is to allow other researchers to supplement the judgements to help create an adaptive test collection which contains supplementary information rather than a completely static collection with binary judgements

Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment

VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided. The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the “Beeldenstorm” collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called “Finding Related Resources Across Languages,” involved linking video to material on the same subject in a different language. Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language “Beeldenstorm” collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names

What-if analysis: A visual analytics approach to Information Retrieval evaluation

This paper focuses on the innovative visual analytics approach realized by the Visual Analytics Tool for Experimental Evaluation (VATE2) system, which eases and makes more effective the experimental evaluation process by introducing the what-if analysis. The what-if analysis is aimed at estimating the possible effects of a modification to an Information Retrieval (IR) system, in order to select the most promising fixes before implementing them, thus saving a considerable amount of effort. VATE2 builds on an analytical framework which models the behavior of the systems in order to make estimations, and integrates this analytical framework into a visual part which, via proper interaction and animations, receives input and provides feedback to the user. We conducted an experimental evaluation to assess the numerical performances of the analytical model and a validation of the visual analytics prototype with domain experts. Both the numerical evaluation and the user validation have shown that VATE2 is effective, innovative, and useful

Evaluating the retrieval effectiveness of Web search engines using a representative query sample

Search engine retrieval effectiveness studies are usually small-scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, data was collected using specialised software, the Relevance Assessment Tool (RAT). We found that while Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3 per cent of cases whereas Bing only found the correct answer 76.6 per cent of the time. We conclude that search engine performance on navigational queries is of great importance, as users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines

Overview of the TREC 2013 Federated Web Search Track

The TREC Federated Web Search track is intended to promote research related to federated search in a realistic web setting, and hereto provides a large data collection gathered from a series of online search engines. This overview paper discusses the results of the first edition of the track, FedWeb 2013. The focus was on basic challenges in federated search: (1) resource selection, and (2) results merging. After an overview of the provided data collection and the relevance judgments for the test topics, the participants’ individual approaches and results on both tasks are discussed. Promising research directions and an outlook on the 2014 edition of the track are provided as well

Alligning Vertical Collection Relevance with User Intent

Selecting and aggregating different types of content from multiple vertical search engines is becoming popular in web search. The user vertical intent, the verticals the user expects to be relevant for a particular information need, might not correspond to the vertical collection relevance, the verticals containing the most relevant content. In this work we propose different approaches to define the set of relevant verticals based on document judgments. We correlate the collection-based relevant verticals obtained from these approaches to the real user vertical intent, and show that they can be aligned relatively well. The set of relevant verticals defined by those approaches could therefore serve as an approximate but reliable ground-truth for evaluating vertical selection, avoiding the need for collecting explicit user vertical intent, and vice versa

Users and Assessors in the Context of INEX: Are Relevance Dimensions Relevant?

The main aspects of XML retrieval are identified by analysing and comparing the following two behaviours: the behaviour of the assessor when judging the relevance of returned document components; and the behaviour of users when interacting with components of XML documents. We argue that the two INEX relevance dimensions, Exhaustivity and Specificity, are not orthogonal dimensions; indeed, an empirical analysis of each dimension reveals that the grades of the two dimensions are correlated to each other. By analysing the level of agreement between the assessor and the users, we aim at identifying the best units of retrieval. The results of our analysis show that the highest level of agreement is on highly relevant and on non-relevant document components, suggesting that only the end points of the INEX 10-point relevance scale are perceived in the same way by both the assessor and the users. We propose a new definition of relevance for XML retrieval and argue that its corresponding relevance scale would be a better choice for INEX

Using Case-Based Learning to Facilitate Clinical Reasoning Across Practice Courses in an Occupational Therapy Curriculum

Although occupational therapy educators have historically used cases as a means to prepare students for clinical practice, there is little evidence that this instructional method actually facilitates clinical reasoning. This convergent, parallel mixed methods study examined how the use of varied case formats, built on the tenets of case-based learning, facilitated specific components of clinical reasoning, and explored how the cases contributed to readiness for professional practice. Case formats included text, video, role-playing, simulated patients, and a client. Case-based learning activities included application of models and frames of reference, conducting assessments, planning and implementing interventions, clinical documentation, and identification of reasoning used. All cases included the opportunity for instructors to provide direct and appropriate feedback, and facilitation of student reflection on their performance. The Self-Assessment of Clinical Reflection and Reasoning (SACRR) was used for quantitative data analysis and detected statistically significant changes in the use of theory and frames of reference to inform practice and in student reasoning about interventions, following case-based learning. Student surveys allowed for pragmatic qualitative analysis, and identified the themes of self-awareness, confidence, and developing competence related to readiness for fieldwork and clinical practice. Student preferences for case format and benefits of varied types of cases were identified. Case-based learning used different case formats, and contributed to the occupational therapy student transition from a clinical reasoning novice to an advanced beginner. Knowledge of this process is useful to occupational therapy educators in structuring case-based learning activities to influencing reasoning