该文提出了一种基于路径与深度的同义词词林词语语义相似度计算方法。该方法通过两个词语义项之间的最短路径以及它们的最近公共父结点在层次树中的深度计算出两个词语义项的相似度。在处理两个词语义项的最短路径与其最近公共父结点的深度时,为提高路径与深度计算的合理性,为分类树中不同层之间的边赋予不同的权值,同时通过两个义项在其最近公共父结点中的分支间距动态调节词语义项间的最短路径,从而平衡两个词语的相似度。该方法修正了目前相关算法只能得出几个固定的相似度值,所有最近公共父结点处于同一层次的义项对之间的相似度都相同的不合理现象,使词语语义相似度的计算结果更为合理。实验表明,该方法对MC30词对的相似度计算值与人工判定值相比,取得了0.856的皮尔逊相关系数,该结果高于目前大多数词语相似度算法与MC30的相关度。
Abstract
In this paper, we propose a word semantic similarity approach based on the path and depth in CiLin. This approach exploits the shortest path between two word senses and the depth of their lowest common parent node in the hierarchy tree to calculate the semantic similarity between two word senses. In order to improve the rationality of calculating the path and depth, we assign different weights to the edges between the different layers in classification tree, while dynamically adjusting the shortest path between two senses through their branch interval in the lowest common parent node. The experiments show that the correlation coefficient between the human judgments in MC30 dataset and the computational measures presented in this approach is 0.856, which is higher than those of most of current semantic similarity algorithms.
关键词
同义词词林 /
路径 /
深度 /
分支间距 /
最近公共父结点
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Key words
CiLin /
path /
depth /
branch interval /
lowest common parent node
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参考文献
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脚注
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基金
国家自然科学基金(61363036,61462010)
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