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Abstract

Das Projekt "Aufbau einer Infrastruktur für Information Retrieval-Evaluationen" (AIIRE) bietet eine Softwareinfrastruktur zur Unterstützung von Information Retrieval-Evaluationen (IR-Evaluationen). Die Infrastruktur basiert auf einem Tool-Kit, das bei GESIS im Rahmen des DFG-Projekts IRM entwickelt wurde. Ziel ist es, ein System zu bieten, das zur Forschung und Lehre am Fachbereich Media für IR-Evaluationen genutzt werden kann. This paper describes some aspects of a project called "Aufbau einer Infrastruktur für Information Retrieval-Evaluationen" (AIIRE). Its goal is to build a software-infrastructure which supports the evaluation of information retrieval algorithms.

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Abstract

Ausgehend von einer Betrachtung in der Rechtsinformatik geläufiger Annahmen zur juristischen Informationserschließung beschreibt der Beitrag wesentliche Ergebnisse einer empirischen Studie der Retrievaleffektivität von Re-cherchen in juristischen Datenbanken. Dabei steht die Frage nach der Notwendigkeit einer intellektuellen Erschließung einerseits, der Effektivität der sogenannten Stichwortsuche andererseits im Mittelpunkt. Die Ergebnisse der Studie, bei der auch ein Vergleich zwischen einem Informationssystem auf der Basis eines Booleschen Retrievalmodells mit einem System auf der Basis statistischer Verfahren vorgenommen wurde, legen den Schluss nahe, dass in der rechtsinformatischen Fachliteratur analytisch begründete Annahmen wie die Gefahr zu großer Antwortmengen bei der Stichwortsuche empirisch nicht zu belegen sind. Auch zeigt sich keine Überlegenheit intellektueller Erschließungsverfahren (Beschlagwortung) gegenüber der automatischen Indexierung, im Gegenteil führt der Einsatz eines statistischen Verfahrens bei identischer Dokumentkollektion zu einer höheren Wiedergewinnungsrate (recall).

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Abstract

Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.

Content

This is an authors' manuscript version of a paper accepted for proceedings of TPDL-2018, Porto, Portugal, Sept 10-13. The nal authenticated publication is available online at https://doi.org/will be added as soon as available.

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