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复杂网络框架下的声音系统自组织

Self-Organization of Sound Systems In the framework of Complex Networks
课程网址: http://videolectures.net/ephdcs08_mukherjee_soossitfocn/  
主讲教师: Animesh Mukherjee
开课单位: 印度理工学院
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
世界语言的声音目录显示出相当程度的对称性。人们假设这种对称性是人类生理、认知和社会因素的反映。尽管对较小的音系的结构作了令人满意的解释,但自1939年以来,辅音的结构一直是一个悬而未决的问题。我们从统计物理的角度重新表述这个问题,更精确地说是复杂的网络,并且观察到音素(辅音和元音)在语言上出现和共存的分布是无标度的。共现网络呈现出强大的社区结构,社区形成背后的驱动力是人的表达和感知因素。为了验证上述原理,我们引入了这些因素的信息论定义——特征熵和特征距离——并证明了自然语言库存在这些方面与随机生成的自然语言库存存在显著差异。基于优先附件的增长模型可以导致出现与真实网络相似的拓扑结构。此外,在另一项研究中,我们观察到辅音共现网络的光谱分析有助于我们归纳语言类型学。
课程简介: The sound inventories of the world's languages show a considerable extent of symmetry. It has been postulated that this symmetry is a reflection of the human physiological, cognitive and societal factors. Although the organization of the vowel systems has been satisfactorily explained for smaller inventories, the structure of the consonant inventories is an open problem since 1939. We reformulate the problem in the light of statistical physics, more precisely complex networks, and observe that the distribution of the occurrence and co-occurrence of the phonemes (consonants and vowels) over languages are scale-free. The co-occurrence network exhibits strong community structures, where the driving forces behind the community formation are the human articulatory and perceptual factors. In order to validate the above principle, we introduce an information theoretic definition of these factors - feature entropy and feature distance - and show that the natural language inventories are significantly different in these terms from the randomly generated ones. A preferential attachment based growth model can lead to the emergence of similar topologies as that of the real networks. Furthermore, in a separate study, we observe that spectral analysis of the co-occurrence network of consonants helps us in the induction of linguistic typologies.
关 键 词: 元音系统; 无标度网络; 自然语言库; 拓扑结构
课程来源: 视频讲座网
最后编审: 2019-11-17:cwx
阅读次数: 44