【摘 要】
：
基于数据的深度学习方法在自然语言处理领域方兴未艾,但种种问题也开始显现.语言的经典的组合原则被打破,让机器“理解”语言变得不再重要,符号之间的黏性完全由样本统计决定,违背了人类语言理解和习得的主观经验.本文针对这一系列问题,详尽阐述了经典数字计算机的运算和决策过程,并对其处理部分模糊性问题时采取的策略进行简要介绍,指出即使是在统计学习过程中,人类偏好也无时无刻不在扮演着关键作用,规律性不可能无条件地从海量数据中自动浮现.逻辑学家和数据科学家有责任揭示这些主观偏好,这不仅是算法可解释性的外在需要,也是提高运
【机 构】
：
内蒙古师范大学法政学院,内蒙古呼和浩特011517;南京大学哲学系,江苏南京210023
论文部分内容阅读
基于数据的深度学习方法在自然语言处理领域方兴未艾,但种种问题也开始显现.语言的经典的组合原则被打破,让机器“理解”语言变得不再重要,符号之间的黏性完全由样本统计决定,违背了人类语言理解和习得的主观经验.本文针对这一系列问题,详尽阐述了经典数字计算机的运算和决策过程,并对其处理部分模糊性问题时采取的策略进行简要介绍,指出即使是在统计学习过程中,人类偏好也无时无刻不在扮演着关键作用,规律性不可能无条件地从海量数据中自动浮现.逻辑学家和数据科学家有责任揭示这些主观偏好,这不仅是算法可解释性的外在需要,也是提高运算效率的正途.
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