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Concretizing the image schema: How semantics guides the bootstrapping of syntax

机译:使图像模式具体化:语义如何引导语法的自举

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In this computational study for acquiring syntactic containment constructions, we suggest that emergent structures in the sensorimotor space may help limit the search for linguistic patterns to a much smaller corpus, in which the earliest glimmerings of syntax may originate. We consider an early learner exposed to perceptual data (video) with co-occurring word-segmented commentaries. In the initial stages of an incremental statistical learning process, content words (e.g. object names) are acquired. Then, some prototypical containment categories emerge by clustering spatial relations in the perceptual input. These situational regularities are used to focus only on sentences uttered during containment situations. By using k-gram and path alignment models on this limited corpus, we show that such a system may begin to learn word categories as well as syntacto-semantic constructions such as “into the X”/[IN(tr,X)]. A key step in the process is the structure in which this perceptual knowledge is organized. The term “image schema” has often been used to refer to such structures, but has widely divergent interpretations. We view the image schema as a category oracle; initially limited to spatio-temporal situation, it may extend, with increasing exposure to language, to new situations.
机译:在此用于获取语法包容结构的计算研究中,我们建议在感觉运动空间中出现的结构可能有助于将对语言模式的搜索限制为一个较小的语料库,在该语料库中可能会出现最早的语法错误。我们认为有一个早期学习者会接触到带有共同词段注释的感知数据(视频)。在增量统计学习过程的初始阶段,将获取内容词(例如对象名称)。然后,通过在感知输入中对空间关系进行聚类,出现了一些典型的包含类别。这些情境规律性仅用于关注收容情况下发出的句子。通过在此有限的语料库上使用k-gram和路径对齐模型,我们证明了这样的系统可能会开始学习单词类别以及诸如“进入X” / [IN(tr,X)]的句法语义结构。该过程中的关键步骤是组织这种知觉知识的结构。术语“图像模式”通常用于指代此类结构,但具有广泛不同的解释。我们将图像架构视为一个类别oracle。最初只限于时空情况,随着语言使用的增加,它可能会扩展到新情况。

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