Complex Structures and Semantics in Free Word Association
Complex Structures and Semantics in Free Word Association
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自由词关联中的复杂结构和语义
DOI：
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发表时间：
2012
影响因子：
0.4
通讯作者：
V. Loreto
中科院分区：
文献类型：
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作者：
Pietro Gravino;V. Servedio;A. Barrat;V. Loreto
我们研究了有向和加权的自由词联想的复杂网络，在这个网络中，玩家根据输入的另一个词写一个词。我们详细分析了两个大的数据集，从两个非常不同的实验：一方面是大规模的多人网络为基础的单词联想游戏被称为人脑云，另一方面是南佛罗里达自由联想规范实验。在这两种情况下，关联网络都表现出相当稳健的性质，如小世界性质、轻微的非对称性以及入度和出度分布之间的强烈不对称性。一个特别有趣的结果涉及存在的一个特征规模的词的联想过程，可以说是与特定的概念背景下，每个字。在将人脑云网络映射到WordNet语义网络之后，我们指出了单词关联在底层语义网络中表示为路径时的基本认知机制。我们推导出特别是一个表达式描述的增长的HBC图，我们强调存在的一个特征尺度的字的关联过程。
We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: On the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases, the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and out-degree distributions. A particularly interesting result concerns the existence of a characteristic scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping, the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network. We derive in particular an expression describing the growth of the HBC graph and we highlight the existence of a characteristic scale for the word association process.