基于语义密度的名词消歧算法

提出了一种以概念相关性为主要依据的名词消歧算法。与现有算法不同的是,该算法在WordNet上对两个语义之间的语义距离进行了拓展,定义了一组语义之间的语义密度,从而量化了一组语义之间的相关性。将相关性转化为语义密度后,再进行消歧。还提出了一种在WordNet上的类似LSH的语义哈希,从而大大降低了语义密度的计算复杂度以及整个消歧算法的计算复杂度。在SemCor上对该算法进行了测试和评估。

Noun Sense Disambiguation Based on Semantic Density

Proposed a novel approach for noun sense disambiguation based on concept correlation. Different from existing algorithms, we extended the notion of semantic distance on WordNet by defining a semantic density for a group of word senses, thus quantizing the correlation among a group of word senses. We disambiguated noun sense after converting the correlation into semantic density. Besides, we also proposed an LSH like semantic hashing on WordNet.With semantic hashing, we greatly reduced the time complexity of calculating semantic density and that of the whole disambiguation algorithm. Experiments and evaluation of this novel approach on SemCor were made.