Temporal multimodal video and lifelog retrieval

The past decades have seen exponential growth of both consumption and production of data, with multimedia such as images and videos contributing significantly to said growth. The widespread proliferation of smartphones has provided everyday users with the ability to consume and produce such content easily. As the complexity and diversity of multimedia data has grown, so has the need for more complex retrieval models which address the information needs of users. Finding relevant multimedia content is central in many scenarios, from internet search engines and medical retrieval to querying one's personal multimedia archive, also called lifelog. Traditional retrieval models have often focused on queries targeting small units of retrieval, yet users usually remember temporal context and expect results to include this. However, there is little research into enabling these information needs in interactive multimedia retrieval. In this thesis, we aim to close this research gap by making several contributions to multimedia retrieval with a focus on two scenarios, namely video and lifelog retrieval. We provide a retrieval model for complex information needs with temporal components, including a data model for multimedia retrieval, a query model for complex information needs, and a modular and adaptable query execution model which includes novel algorithms for result fusion. The concepts and models are implemented in vitrivr, an open-source multimodal multimedia retrieval system, which covers all aspects from extraction to query formulation and browsing. vitrivr has proven its usefulness in evaluation campaigns and is now used in two large-scale interdisciplinary research projects. We show the feasibility and effectiveness of our contributions in two ways: firstly, through results from user-centric evaluations which pit different user-system combinations against one another. Secondly, we perform a system-centric evaluation by creating a new dataset for temporal information needs in video and lifelog retrieval with which we quantitatively evaluate our models. The results show significant benefits for systems that enable users to specify more complex information needs with temporal components. Participation in interactive retrieval evaluation campaigns over multiple years provides insight into possible future developments and challenges of such campaigns

On the evaluation of aggregated web search

Aggregating search results from a variety of heterogeneous sources or so-called verticals such as news, image and video into a single interface is a popular paradigm in web search. This search paradigm is commonly referred to as aggregated search. The heterogeneity of the information, the richer user interaction, and the more complex presentation strategy, make the evaluation of the aggregated search paradigm quite challenging. The Cranfield paradigm, use of test collections and evaluation measures to assess the effectiveness of information retrieval (IR) systems, is the de-facto standard evaluation strategy in the IR research community and it has its origins in work dating to the early 1960s. This thesis focuses on applying this evaluation paradigm to the context of aggregated web search, contributing to the long-term goal of a complete, reproducible and reliable evaluation methodology for aggregated search in the research community. The Cranfield paradigm for aggregated search consists of building a test collection and developing a set of evaluation metrics. In the context of aggregated search, a test collection should contain results from a set of verticals, some information needs relating to this task and a set of relevance assessments. The metrics proposed should utilize the information in the test collection in order to measure the performance of any aggregated search pages. The more complex user behavior of aggregated search should be reflected in the test collection through assessments and modeled in the metrics. Therefore, firstly, we aim to better understand the factors involved in determining relevance for aggregated search and subsequently build a reliable and reusable test collection for this task. By conducting several user studies to assess vertical relevance and creating a test collection by reusing existing test collections, we create a testbed with both the vertical-level (user orientation) and document-level relevance assessments. In addition, we analyze the relationship between both types of assessments and find that they are correlated in terms of measuring the system performance for the user. Secondly, by utilizing the created test collection, we aim to investigate how to model the aggregated search user in a principled way in order to propose reliable, intuitive and trustworthy evaluation metrics to measure the user experience. We start our investigations by studying solely evaluating one key component of aggregated search: vertical selection, i.e. selecting the relevant verticals. Then we propose a general utility-effort framework to evaluate the ultimate aggregated search pages. We demonstrate the fidelity (predictive power) of the proposed metrics by correlating them to the user preferences of aggregated search pages. Furthermore, we meta-evaluate the reliability and intuitiveness of a variety of metrics and show that our proposed aggregated search metrics are the most reliable and intuitive metrics, compared to adapted diversity-based and traditional IR metrics. To summarize, in this thesis, we mainly demonstrate the feasibility to apply the Cranfield Paradigm for aggregated search for reproducible, cheap, reliable and trustworthy evaluation

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Joint analysis of user-generated content and product information to enhance user experience in e-commerce

The development of Internet has brought us a more convenient way to purchase goods through e-commerce, which has gradually pervaded our life. However, shopping experience of users in e-commerce has been far from the optimum. In order to enhance user experience in e-commerce, we propose a series of novel studies based on joint analysis of user-generated content and product information; in this dissertation, user-generated content includes user reviews and social media text data, and product information includes product descriptions and product specifications in general. This dissertation aims at assisting e-commerce users in two directions: discovering products and making purchase decisions. To help users discover products, we first propose to leverage user reviews to improve accuracy of product search. We carefully combine product descriptions and user reviews to improve product search. Then, we also propose to recommend products via inference of implicit intent in social media text. We infer implicit intent in user status text leveraging parallel corpora we build from social media, and we recommend products whose descriptions satisfy the inferred intent. In order to help users make purchase decisions, we first propose to generate augmented product specifications leveraging user reviews. Product specifications are often difficult to understand especially for high-technology products that contain many advanced features. We jointly model user reviews and product specifications to augment product specifications with useful information in the user reviews. We also propose to retrieve relevant opinions for new products. New or unpopular products often have no reviews, and such lack of information makes consumers hesitate to make a purchase decision. We leverage user reviews of similar products, where similarity is estimated using product specifications, to retrieve relevant opinions for new products. The experiment results show the proposed models are effective in general. The models are also general enough to be applied to any entities with their text data. Furthermore, the models can benefit both product manufacturers and consumers, so their potential impact may be even bigger

Crowdsourcing construction of information retrieval test collections for conversational speech

textBuilding a test collection for an ad hoc information retrieval system on conversational speech raises new challenges for researchers. Traditional methods for building test collections are costly, and thus they are not feasible to apply to large scale conversational speech data. Constructing a large test collection on conversational speech with high quality at low cost is challenging. Crowdsourcing may represent a promising approach. Crowd workers tend to be less expensive than professional assessors, and crowd workers can work simultaneously to perform jobs on a large scale. However, despite the benefits of scale and cost, the quality of the results delivered by crowd workers may suffer. This thesis focuses on relevance judging, one of the key components of a test collection. We adopt two crowdsourcing platforms: oDesk and MTurk, use audio clips and various versions of transcripts, conduct multiple experiments under diverse settings, and analyze the results qualitatively and quantitatively. We delve into what factors influence the quality of relevance judgments on conversational speech. We also investigate differences between relevance judgements from experts and crowd workers. This thesis also describes best practices for the design of crowdsourcing tasks to improve crowd workers' performance. Ultimately, these may assist researchers in building high-quality test collections on conversational speech at low cost and scale through crowdsourcing.Informatio

Evaluation Methodologies for Visual Information Retrieval and Annotation

Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt Performanz und Qualität der Informationsgewinnung zu bewerten. Bereits in den 60er Jahren wurden erste Methodologien für die system-basierte Evaluation aufgestellt und in den Cranfield Experimenten überprüft. Heutzutage gehören Evaluation, Test und Qualitätsbewertung zu einem aktiven Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten Methoden. Evaluationsmethoden fanden zunächst in der Bewertung von Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von Multimediaanalyse-Systeme übertragen. Dies geschah häufig, ohne die Evaluationsmethoden in Frage zu stellen oder sie an die veränderten Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschäftigt sich mit der system-basierten Evaluation von Indizierungssystemen für Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von Annotationen: Nutzeranforderungen für das Suchen und Verschlagworten von Bildern, Evaluationsmaße für die Qualitätsbewertung von Indizierungssystemen und Anforderungen an die Erstellung visueller Testkollektionen. Am Beispiel der Evaluation automatisierter Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer zuverlässigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt und Evaluationsmaße zur Qualitätsbewertung eingeführt, analysiert und experimentell verglichen. Traditionelle Maße zur Ermittlung der Performanz werden in vier Dimensionen klassifiziert. Evaluationsmaße vergeben üblicherweise binäre Kosten für korrekte und falsche Annotationen. Diese Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin überprüft werden. In dieser Arbeit wird aufgezeigt, wie semantische Ähnlichkeiten visueller Konzepte automatisiert abgeschätzt und in den Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit inkludieren ein Nutzermodell für die konzeptbasierte Suche von Bildern, eine vollständig bewertete Testkollektion und neue Evaluationsmaße für die anforderungsgerechte Qualitätsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information Retrieval (IR) systems. Starting with the Cranfield experiments in the early 60ies, methodologies for the system-based performance assessment emerged and established themselves, resulting in an active research field with a number of successful benchmarking activities. With the rise of the digital age, procedures of text retrieval evaluation were often transferred to multimedia retrieval evaluation without questioning their direct applicability. This thesis investigates the problem of system-based performance assessment of annotation approaches in generic image collections. It addresses three important parts of annotation evaluation, namely user requirements for the retrieval of annotated visual media, performance measures for multi-label evaluation, and visual test collections. Using the example of multi-label image annotation evaluation, I discuss which concepts to employ for indexing, how to obtain a reliable ground truth to moderate costs, and which evaluation measures are appropriate. This is accompanied by a thorough analysis of related work on system-based performance assessment in Visual Information Retrieval (VIR). Traditional performance measures are classified into four dimensions and investigated according to their appropriateness for visual annotation evaluation. One of the main ideas in this thesis adheres to the common assumption on the binary nature of the score prediction dimension in annotation evaluation. However, the predicted concepts and the set of true indexed concepts interrelate with each other. This work will show how to utilise these semantic relationships for a fine-grained evaluation scenario. Outcomes of this thesis result in a user model for concept-based image retrieval, a fully assessed image annotation test collection, and a number of novel performance measures for image annotation evaluation