Characterising Semantic Relatedness using Interpretable Directions in Conceptual Spaces
Characterising Semantic Relatedness using Interpretable Directions in Conceptual Spaces
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DOI：
10.3233/978-1-61499-419-0-243
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
J. Derrac;Steven Schockaert
中科院分区：
文献类型：
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作者：
J. Derrac;Steven Schockaert
各种应用，如基于评论的推荐系统和类比分类器，依赖于不同实体如何关联的知识。在本文中，我们提出了一种方法来识别这样的语义关系，通过解释它们作为定性的空间关系的概念空间。特别是，我们使用多维缩放从相关的文本语料库中诱导概念空间，然后以完全无监督的方式识别与相对属性（如“比”更暴力”）相对应的方向。我们还展示了FOIL的一个变体是如何能够从这样的定性表示中学习自然类别的，通过模拟一个更有利的推理，一个重要的常识推理模式。
Various applications, such as critique-based recommendation systems and analogical classifiers, rely on knowledge of how different entities relate. In this paper, we present a methodology for identifying such semantic relationships, by interpreting them as qualitative spatial relations in a conceptual space. In particular, we use multi-dimensional scaling to induce a conceptual space from a relevant text corpus and then identify directions that correspond to relative properties such as "more violent than" in an entirely unsupervised way. We also show how a variant of FOIL is able to learn natural categories from such qualitative representations, by simulating a fortiori inference, an important pattern of commonsense reasoning.