Johannes Leveling

From WikiPapers
Jump to: navigation, search

Johannes Leveling is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Multi-platform image search using tag enrichment Document expansion
Image retrieval
Query formulation
Relevance feedback
SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval English 2012 The number of images available online is growing steadily and current web search engines have indexed more than 10 billion images. Approaches to image retrieval are still often text-based and operate on image annotations and captions. Image annotations (i.e. image tags) are typically short, user-generated, and of varying quality, which increases the mismatch problem between query terms and image tags. For example, a user might enter the query "wedding dress" while all images are annotated with "bridal gown" or "wedding gown". This demonstration presents an image search system using reduction and expansion of image annotations to overcome vocabulary mismatch problems by enriching the sparse set of image tags. Our image search application accepts a written query as input and produces a ranked list of result images and annotations (i.e. image tags) as output. The system integrates methods to reduce and expand the image tag set, thus decreasing the effect of sparse image tags. It builds on different image collections such as the Wikipedia image collection (http://www.imageclef.org/wikidata) and the Microsoft Office.com ClipArt collection (http://office.microsoft.com/), but can be applied to social collections such as Flickr as well. Our demonstration system runs on PCs, tablets, and smartphones, making use of advanced user interface capabilities on mobile devices. 0 0
Document expansion for text-based image retrieval at CLEF 2009 Lecture Notes in Computer Science English 2010 In this paper, we describe and analyze our participation in the WikipediaMM task at CLEF 2009. Our main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection - DBpedia. In our experiments, we use the Okapi feedback algorithm for document expansion. Compared with our text retrieval baseline, our best document expansion RUN improves MAP by 17.89%. As one of our conclusions, document expansion from external resource can play an effective factor in the image metadata retrieval task. 0 0
Recursive question decomposition for answering complex geographic questions Lecture Notes in Computer Science English 2010 This paper describes the GIRSA-WP system and the experiments performed for GikiCLEF 2009, the geographic information retrieval task in the question answering track at CLEF 2009. Three runs were submitted. The first one contained only results from the InSicht QA system; it showed high precision, but low recall. The combination with results from the GIR system GIRSA increased recall considerably, but reduced precision. The second run used a standard IR query, while the third run combined such queries with a Boolean query with selected keywords. The evaluation showed that the third run achieved significantly higher mean average precision (MAP) than the second run. In both cases, integrating GIR methods and QA methods was successful in combining their strengths (high precision of deep QA, high recall of GIR), resulting in the third-best performance of automatic runs in GikiCLEF. The overall performance still leaves room for improvements. For example, the multilingual approach is too simple. All processing is done in only one Wikipedia (the German one); results for the nine other languages are collected by following the translation links in Wikipedia. 0 0
GikiP at GeoCLEF 2008: joining GIR and QA forces for querying Wikipedia CLEF English 2009 0 0
GikiP at geoCLEF 2008: Joining GIR and QA forces for querying wikipedia Lecture Notes in Computer Science English 2009 This paper reports on the GikiP pilot that took place in 2008 in GeoCLEF. This pilot task requires a combination of methods from geographical information retrieval and question answering to answer queries to the Wikipedia. We start by the task description, providing details on topic choice and evaluation measures. Then we offer a brief motivation from several perspectives, and we present results in detail. A comparison of participants' approaches is then presented, and the paper concludes with improvements for the next edition. 0 0
Coreference resolution for questions and answer merging by validation Lecture Notes in Computer Science English 2008 For its fourth participation at QA@CLEF, the German question answering (QA) system InSicht was improved for CLEF 2007 in the following main areas: questions containing pronominal or nominal anaphors are treated by a coreference resolver; the shallow QA methods are improved; and a specialized module is added for answer merging. Results showed a performance drop compared to last year mainly due to problems in handling the newly added Wikipedia corpus. However, dialog treatment by coreference resolution delivered very accurate results so that follow-up questions can be handled similarly to isolated questions. 0 0