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SuMACC Project's Corpus: A Topic-Based Query Extension Approach to Retrieve Multimedia Documents

机译:SUMACC项目的语料库:一种基于主题的查询扩展方法来检索多媒体文档

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The SuMACC project aims at automatically tracking new multimodal entities on Internet. The goal of the project is to propose robust multimedia methods that define relevant patterns allowing to automatically retrieve these entities. This paper describes the SuMACC corpus collected on video-sharing platforms using word-queries. Since concepts are limited to a single or few words, querying video-sharing platforms with the concept only can easily introduce irrelevant collected videos. In this paper, we propose to use an extended query obtained by mapping the initial concept into a topic space from a Latent Dirichlet Allocation (LDA) algorithm. This topic-based query extension approach allows to better retrieve videos related to the targeted concept. As a result, a corpus of 7,517 videos, extracted using the simple (i.e. concept only) and the extended queries, from 47 concepts, was obtained. Results show the effectiveness of the proposed thematic querying approach compared to the simple concept query in terms of relevance (+21%) and ambiguity (-4%). The annotation process as well as the corpus statistics are detailed in this paper.
机译:SUMACC项目旨在在Internet上自动跟踪新的多模式实体。该项目的目标是提出鲁棒的多媒体方法,这些方法定义了相关模式,允许自动检索这些实体。本文介绍了使用Word-Qualies在视频共享平台上收集的SUMACCCACC语料库。由于概念仅限于单个或少数单词,因此使用该概念查询视频共享平台只能轻易引入无关收集的视频。在本文中,我们建议使用通过将初始概念映射到来自潜在Dirichlet分配(LDA)算法的主题空间中获得的扩展查询。基于主题的查询扩展方法允许更好地检索与目标概念相关的视频。结果,获得了使用简单(即概念)和47个概念的简单(即概念)提取的7,517个视频的语料库。结果表明,在相关性(+ 21%)和歧义(-4%)中,建议主题查询方法的有效性。本文详述了注释过程以及语料库统计。

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