Nadig et al. (2008) proposed the most famous method for lemmas validation, which is Validate Synset algorithm. It relys on dictionary definitions to verify synset, and they emphasize that a synset in a given Worknet should be both complete and correct. 

Lemmas Validation Methods

Lemmas clustering methods retrieve and form clusters for collected snyset lemmas from LSR and dictionaries based on the lexical semantic network. For example, Lam et al. (2014) proposed used machine translation to build a new Wordnet from several lexical resources and Wordnets.

Lemmas Clustering Methods

Based on how the synset lemmas are assessed, synset lemmas evaluation methods can be categorized as:

1)	Lemmas Validation Methods

2)	Lemmas Clustering Methods.



University of Notre Dame

A good LSR depends on synset correctness and completeness, and also depends on the connectivity degree of the synset with other synsets in the semantic network. There are different evaluation methods for each part of the LSR:

1)	Synset Lemmas Evaluation Methods

2)	Synset Gloss Evaluation Methods

3)	Synset Relations Evaluation Methods


Quality Evaluation Methods for LSR

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

Synset Lemmas Evaluation Methods

Synset gloss contains background information about the synset. To evaluate it, Zhang et al. (2011) introduced a model that can measure semantic relatedness between gloss and synset, where they used the WordNet gloss and semantic relations as features that builds concept vectors. 

Synset Gloss Evaluation Methods

There are two types of synset relations corresponding evaluation methods:

1)	Implicit Relations

2)	Special Relations


Learn Before

Related

Learn After