Conceptualizing syntactic categories as semantic categories: Unifying part-of-speech identification and semantics using co-occurrence vector averaging
Conceptualizing syntactic categories as semantic categories: Unifying part-of-speech identification and semantics using co-occurrence vector averaging
复制标题
将句法类别概念化为语义类别：使用共现向量平均来统一词性识别和语义
DOI：
--
复制
发表时间：
2018
影响因子：
5.4
通讯作者：
Geoff Hollis
中科院分区：
文献类型：
--
作者：
C. Westbury;Geoff Hollis
共现模型一直是心理学家相当感兴趣的，因为它们是建立在非常简单的功能。在预测模型的情况下，这一点尤其明显，例如Mikolov，Chen，Corrado和Dean（2013）引入的连续跳跃模型，因为这些模型依赖于与非人类动物判别学习的简单Rescorla-瓦格纳模型密切相关的功能（Rescorla &瓦格纳，1972），该模型在心理学中作为许多动物学习过程的模型具有丰富的历史。我们复制和扩展早期的工作表明，它是可以提取准确的信息直接从字同现模式的句法类别和形态家族成员，并提供证据，从四个实验表明，这些信息预测人类的反应时间和准确性类成员的决定。
Co-occurrence models have been of considerable interest to psychologists because they are built on very simple functionality. This is particularly clear in the case of prediction models, such as the continuous skip-gram model introduced in Mikolov, Chen, Corrado, and Dean (2013), because these models depend on functionality closely related to the simple Rescorla–Wagner model of discriminant learning in nonhuman animals (Rescorla & Wagner, 1972), which has a rich history within psychology as a model of many animal learning processes. We replicate and extend earlier work showing that it is possible to extract accurate information about syntactic category and morphological family membership directly from patterns of word co-occurrence, and provide evidence from four experiments showing that this information predicts human reaction times and accuracy for class membership decisions.
影响因子：
9.5
作者：
G. Marcus;S. Pinker;M. Ullman;Michelle A. Hollander;T. Rosen;Fei Xu;H. Clahsen
通讯作者：
G. Marcus;S. Pinker;M. Ullman;Michelle A. Hollander;T. Rosen;Fei Xu;H. Clahsen