Marco-Elia-Nam (MEN) Semantic Word Relatedness Measure

Example(s):
- …
Counter-Example(s):
- a Semantic Similarity Measure,
- a Semantic Sentence Relatedness Measure.
See: Natural Language Processing, Natural Language Understanding, Language Model, Semantics, Syntactic.

References

(Chandrasekaran & Mago, 2021) ⇒ Dhivya Chandrasekaran, and Vijay Mago. (2021). “Evolution of Semantic Similarity - A Survey.” In: ACM Computing Surveys, 54(2).
- QUOTE: Semantic similarity methods usually give a ranking or percentage of similarity between texts, rather than a binary decision as similar or not similar. Semantic similarity is often used synonymously with semantic relatedness. However, semantic relatedness not only accounts for the semantic similarity between texts but also considers a broader perspective analyzing the shared semantic properties of two words. For example, the words ‘coffee’ and ‘mug’ may be related to one another closely, but they are not considered semantically similar whereas the words ‘coffee’ and ‘tea’ are semantically similar. Thus, semantic similarity may be considered, as one of the aspects of semantic relatedness. The semantic relationship including similarity is measured in terms of semantic distance, which is inversely proportional to the relationship (...)* See: Semantic Similarity Measure, Word Relatedness Measure, Distributional Semantics, Multimodal Semantic Relatedness Model.

(Bruni et al., 2014) ⇒ Elia Bruni, Nam-Khanh Tran, and Marco Baroni. (2014). "Multimodal Distributional Semantics". In: Journal of Artificial Intelligence Research, 49.
- QUOTE: The high-score MEN pairs include not only pairs of terms that are strictly taxonomically close (cathedral-church: 0.94) but also terms that are connected by broader semantic relations, such as whole-part (flower-petal: 0.92), item and related event (boat-fishing: 0.9),etc. For this reason, we prefer to refer to MEN as a semantic relatedness rather than similarity score data set. Note that WS is also capturing a broader notion of relatedness (Agirre et al., 2009). MEN is publicly available and it can be downloaded from: http://clic.cimec.unitn.it/~elia.bruni/MEN