Using Visual Information to Predict Lexical Preference
Using Visual Information to Predict Lexical Preference
复制标题
使用视觉信息预测词汇偏好
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
R. Goebel
中科院分区：
文献类型：
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作者：
S. Bergsma;R. Goebel
大多数NLP系统仅基于语言（文本或口语）输入进行预测。我们展示了如何使用视觉信息来做出更好的语言预测。我们专注于选择性偏好，具体来说，确定合理的名词参数特定的动词谓词。对于每个参数名词，我们从网络上相应的图像中提取视觉特征。对于每个动词谓词，我们训练一个分类器来选择指示其首选参数的视觉特征。我们表明，对于某些动词，使用视觉信息可以显着提高性能超过基线。对于成功的情况下，视觉信息是有用的，即使在共生信息来自网络规模的文本的存在。我们评估了各种训练配置，这些配置在视觉特征，图像采集方法和图像数量方面有所不同。
Most NLP systems make predictions based solely on linguistic (textual or spoken) input. We show how to use visual information to make better linguistic predictions. We focus on selectional preference; specifically, determining the plausible noun arguments for particular verb predicates. For each argument noun, we extract visual features from corresponding images on the web. For each verb predicate, we train a classifier to select the visual features that are indicative of its preferred arguments. We show that for certain verbs, using visual information can significantly improve performance over a baseline. For the successful cases, visual information is useful even in the presence of cooccurrence information derived from webscale text. We assess a variety of training configurations, which vary over classes of visual features, methods of image acquisition, and numbers of images.