InfoLink: analysis of Dutch broadcast news and cross-media browsing

In this paper, a cross-media browsing demonstrator named InfoLink is described. InfoLink automatically links the content of Dutch broadcast news videos to related information sources in parallel collections containing text and/or video. Automatic segmentation, speech recognition and available meta-data are used to index and link items. The concept is visualised using SMIL-scripts for presenting the streaming broadcast news video and the information links

The Infinite Index: Information Retrieval on Generative Text-To-Image Models

Conditional generative models such as DALL-E and Stable Diffusion generate images based on a user-defined text, the prompt. Finding and refining prompts that produce a desired image has become the art of prompt engineering. Generative models do not provide a built-in retrieval model for a user's information need expressed through prompts. In light of an extensive literature review, we reframe prompt engineering for generative models as interactive text-based retrieval on a novel kind of "infinite index". We apply these insights for the first time in a case study on image generation for game design with an expert. Finally, we envision how active learning may help to guide the retrieval of generated images.Comment: Final version for CHIIR 202

Is searching full text more effective than searching abstracts?

<p>Abstract</p> <p>Background</p> <p>With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE^®abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: <it>bm25 </it>and the ranking algorithm implemented in the open-source Lucene search engine.</p> <p>Results</p> <p>Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles.</p> <p>Conclusion</p> <p>Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations.</p

Mining Meaning from Wikipedia

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.Comment: An extensive survey of re-using information in Wikipedia in natural language processing, information retrieval and extraction and ontology building. Accepted for publication in International Journal of Human-Computer Studie

Search beyond traditional probabilistic information retrieval

"This thesis focuses on search beyond probabilistic information retrieval. Three ap- proached are proposed beyond the traditional probabilistic modelling. First, term associ- ation is deeply examined. Term association considers the term dependency using a factor analysis based model, instead of treating each term independently. Latent factors, con- sidered the same as the hidden variables of ""eliteness"" introduced by Robertson et al. to gain understanding of the relation among term occurrences and relevance, are measured by the dependencies and occurrences of term sequences and subsequences. Second, an entity-based ranking approach is proposed in an entity system named ""EntityCube"" which has been released by Microsoft for public use. A summarization page is given to summarize the entity information over multiple documents such that the truly relevant entities can be highly possibly searched from multiple documents through integrating the local relevance contributed by proximity and the global enhancer by topic model. Third, multi-source fusion sets up a meta-search engine to combine the ""knowledge"" from different sources. Meta-features, distilled as high-level categories, are deployed to diversify the baselines. Three modified fusion methods are employed, which are re- ciprocal, CombMNZ and CombSUM with three expanded versions. Through extensive experiments on the standard large-scale TREC Genomics data sets, the TREC HARD data sets and the Microsoft EntityCube Web collections, the proposed extended models beyond probabilistic information retrieval show their effectiveness and superiority.

Retrieval Enhancements for Task-Based Web Search

The task-based view of web search implies that retrieval should take the user perspective into account. Going beyond merely retrieving the most relevant result set for the current query, the retrieval system should aim to surface results that are actually useful to the task that motivated the query. This dissertation explores how retrieval systems can better understand and support their users’ tasks from three main angles: First, we study and quantify search engine user behavior during complex writing tasks, and how task success and behavior are associated in such settings. Second, we investigate search engine queries formulated as questions, and explore patterns in a large query log that may help search engines to better support this increasingly prevalent interaction pattern. Third, we propose a novel approach to reranking the search result lists produced by web search engines, taking into account retrieval axioms that formally specify properties of a good ranking.Die Task-basierte Sicht auf Websuche impliziert, dass die Benutzerperspektive berücksichtigt werden sollte. Über das bloße Abrufen der relevantesten Ergebnismenge für die aktuelle Anfrage hinaus, sollten Suchmaschinen Ergebnisse liefern, die tatsächlich für die Aufgabe (Task) nützlich sind, die diese Anfrage motiviert hat. Diese Dissertation untersucht, wie Retrieval-Systeme die Aufgaben ihrer Benutzer besser verstehen und unterstützen können, und leistet Forschungsbeiträge unter drei Hauptaspekten: Erstens untersuchen und quantifizieren wir das Verhalten von Suchmaschinenbenutzern während komplexer Schreibaufgaben, und wie Aufgabenerfolg und Verhalten in solchen Situationen zusammenhängen. Zweitens untersuchen wir Suchmaschinenanfragen, die als Fragen formuliert sind, und untersuchen ein Suchmaschinenlog mit fast einer Milliarde solcher Anfragen auf Muster, die Suchmaschinen dabei helfen können, diesen zunehmend verbreiteten Anfragentyp besser zu unterstützen. Drittens schlagen wir einen neuen Ansatz vor, um die von Web-Suchmaschinen erstellten Suchergebnislisten neu zu sortieren, wobei Retrieval-Axiome berücksichtigt werden, die die Eigenschaften eines guten Rankings formal beschreiben

A survey on tree matching and XML retrieval

International audienceWith the increasing number of available XML documents, numerous approaches for retrieval have been proposed in the literature. They usually use the tree representation of documents and queries to process them, whether in an implicit or explicit way. Although retrieving XML documents can be considered as a tree matching problem between the query tree and the document trees, only a few approaches take advantage of the algorithms and methods proposed by the graph theory. In this paper, we aim at studying the theoretical approaches proposed in the literature for tree matching and at seeing how these approaches have been adapted to XML querying and retrieval, from both an exact and an approximate matching perspective. This study will allow us to highlight theoretical aspects of graph theory that have not been yet explored in XML retrieval

Leveraging Semantic Annotations for Event-focused Search & Summarization

Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.Im heutigen Big Data Zeitalters existieren überwältigende Mengen an Textinformationen, die über mehrere Quellen verteilt sind und ein hohes Maß an Redundanz haben. Durch diese Gegebenheiten ist eine Retroperspektive auf vergangene Ereignisse für Konsumenten nur schwer möglich. Eine plausible Lösung ist die Verknüpfung semantisch ähnlicher, aber über mehrere Quellen verteilter Informationen, um dadurch eine Struktur zu erzwingen, die mehrere Zugriffspfade auf relevante Informationen, bietet. Vor diesem Hintergrund benutzt diese Dissertation Wikipedia und Onlinenachrichten als zwei prominente, aber dennoch grundverschiedene Informationsquellen, um die folgenden drei Probleme anzusprechen: • Wir adressieren ein Verknüpfungsproblem, um Wikipedia-Auszüge mit Nachrichtenartikeln zu verbinden und das Problem in eine Information-Retrieval-Aufgabe umzuwandeln. Unser neuartiger Ansatz integriert Zeit- und Geobezüge sowie Entitäten mit Text, um relevante Dokumente, die mit einem gegebenen Auszug verknüpft werden können, zu identifizieren. • Wir befassen uns mit einer unüberwachten Extraktionsmethode zur automatischen Zusammenfassung von Texten aus mehreren Dokumenten um Ereigniszusammenfassungen mit fester Länge zu generieren, was eine effiziente Aufnahme von Informationen aus großen Dokumentenmassen ermöglicht. Unser neuartiger Ansatz schlägt eine ganzzahlige lineare Optimierungslösung vor, die globale Inferenzen über Text, Zeit, Geolokationen und mit Ereignis-verbundenen Entitäten zieht. • Um den zeitlichen Fokus kurzer Ereignisbeschreibungen abzuschätzen, stellen wir einen semi-überwachten Ansatz vor, der die Redundanz innerhalb einer langzeitigen Dokumentensammlung ausnutzt, um genaue probabilistische Zeitmodelle abzuschätzen. Umfangreiche experimentelle Auswertungen zeigen die Wirksamkeit und Tragfähigkeit unserer vorgeschlagenen Ansätze zur Erreichung des größeren Ziels