Using fMRI activation to conceptual stimuli to evaluate methods for extracting conceptual representations from corpora
Using fMRI activation to conceptual stimuli to evaluate methods for extracting conceptual representations from corpora
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发表时间：
2010-06
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通讯作者：
Barry Devereux;Colin Kelly;A. Korhonen
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作者：
Barry Devereux;Colin Kelly;A. Korhonen
我们提出了一系列从语料库中获得概念表征的方法，并研究了Mitchell等人（2008）的fMRI数据和机器学习方法的有用性，作为评估不同模型的基础。在这个框架内，语义模型的质量是量化的，其预测与概念刺激相关的fMRI激活的能力。Mitchell等人使用手动获得的动词集作为其语义模型的基础;在本文中，我们还考虑了自动获得的特征规范类语义表示。这些模型对语料库中与表示概念知识相关的可用信息类型做出了不同的假设。我们的研究结果表明，自动获得的表征可以对与刺激相关的大脑活动做出同样强大的预测。
We present a series of methods for deriving conceptual representations from corpora and investigate the usefulness of the fMRI data and machine learning methodology of Mitchell et al. (2008) as a basis for evaluating the different models. Within this framework, the quality of a semantic model is quantified by its ability to predict the fMRI activation associated with conceptual stimuli. Mitchell et al. used a manually-acquired set of verbs as the basis for their semantic model; in this paper, we also consider automatically acquired feature-norm-like semantic representations. These models make different assumptions about the kinds of information available in corpora that is relevant to representing conceptual knowledge. Our results indicate that automatically-acquired representations can make equally powerful predictions about the brain activity associated with the stimuli.