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A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces

机译:一种资源驱动的方法,将语言资源锚定到概念空间

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In this paper we introduce the ttcs system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. ttcs takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.
机译:在本文中,我们介绍了ttcs系统,该系统以术语概念空间命名,该系统利用了依赖BabelNet,NASARI和ConceptNet的资源驱动方法。 ttcs输入一个术语及其用法上下文,并输出特定类型的基于矢量的语义表示作为输出,其中概念信息通过“概念空间”(常识知识表示和推理的几何框架)进行编码。该系统已通过两次实验进行了评估。在第一种情况下,我们使用在线问卷评估了关于人类判断的常识性概念信息的质量。在第二篇文章中,我们比较了概念分类系统的性能,该系统运行了两次,一次是提取注释,一次是手工注释。在这两种情况下,结果都是令人鼓舞的,并提供了宝贵的见识以进行实质性的改进。

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