Special relations add more semantic properties on the synset lattice in lexicons. For example,  Hayashi (2016) proposed an approach that analyzed evocation relation that might hold between a pair of lexicalized concepts evocation dataset. They first used neural network model to classify evocation relations into 4 categories, and then used forest regression to predict evocation strength.

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There are two types of synset relations corresponding evaluation methods:

1)	Implicit Relations

2)	Special Relations


Synset Relations Evaluation Methods

Khalilia, H., Freihat, A. A., & Giunchiglia, F. (2021). The Quality of Lexical Semantic Resources: A Survey. In Proceedings of The Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021) (pp. 117-129).

The Quality of Lexical Semantic Resources: A Survey

A group of methods evaluate synset relation quality by using implicit relations between synset. For example, Nadig et al. (2008) proposed an approach that receives two synsets as input and states and judge whether they have a hypernym-hyponym relationship. In their method, firstly ”prefix forms as an indicator of hypernymy”, secondly ”using web search to validate hypernymy”, and lastly “using coordinate terms to validate hypernymy”.

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