NASARI (Novel Approach to a Semantically-Aware Representation of Items) System

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A NASARI (Novel Approach to a Semantically-Aware Representation of Items) System is a Multilingual Semantic Vector Representation System for BabelNet synsets and Wikipedia pages.



References

2021

  1. Please note that BabelNet covers WordNet and Wikipedia among other resources, enabling our vectors to be applicable for representations of concepts and named entities in each of these resources.

2017

2016

1. Semantic similarity. Nasari proved to be highly reliable in the task of semantic similarity measurement, as it provides state-of-the-art performance on several datasets across different evaluation benchmarks:
2. Sense clustering. We constructed a highly competitive unsupervised system on the basis of the Nasari representations, outperforming state-of-the-art supervised systems on two manually-annotated Wikipedia sense clustering datasets (...).
3. Domain labeling. We used our system for annotating synsets of a large lexical semantic resource (BabelNet), and benchmarked our system against three automatic baselines on two gold standard datasets:(...)
4. Word Sense Disambiguation. We proposed a simple framework for a knowledge-rich unsupervised disambiguation system. Our system obtained state-of-the-art results on multilingual All-Words Word Sense Disambiguation using Wikipedia as sense inventory, evaluated on the SemEval-2013 dataset (...)

2015