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Grounding co-occurrence: Identifying features in a lexical co-occurrence model of semantic memory

机译:基础共现:在语义记忆的词汇共现模型中识别特征

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Lexical co-occurrence models of semantic memory represent word meaning by vectors in a high-dimensional space. These vectors are derived from word usage, as found in a large corpus of written text. Typically, these models are fully automated, an advantage over models that represent semantics that are based on human judgments (e.g., feature-based models). A common criticism of co-occurrence models is that the representations are not grounded: Concepts exist only relative to each other in the space produced by the model. It has been claimed that feature -based models offer an advantage in this regard. In this article, we take a step toward grounding a cooccurrence model. A feed-forward neural network is trained using back propagation to provide a mapping from co-oc< urrence vectors to feature norms collected from subjects. We show that this network is able to retrieve the features of a concept from its co-occurrence vector with high accuracy and is able to generalize this ability to produce an appropriate list of features from the co-occurrence vector of a novel concept.
机译:语义记忆的词汇共现模型通过向量在高维空间中表示单词的含义。这些向量是从大量书面文字中发现的单词用法中得出的。通常,这些模型是完全自动化的,这优于表示基于人类判断的语义的模型(例如,基于特征的模型)的优势。对共现模型的普遍批评是,表示形式不扎根:概念仅在模型产生的空间中相对存在。据称,基于特征的模型在这方面具有优势。在本文中,我们朝着建立共生模型迈出了一步。使用反向传播训练前馈神经网络,以提供从共现向量到从受试者收集的特征范数的映射。我们表明,该网络能够从其共现向量中高精度检索概念的特征,并且能够概括这种从新概念的共现向量产生适当特征列表的能力。

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