Paper: MELODI: Semantic Similarity of Words and Compositional Phrases using Latent Vector Weighting

ACL ID S13-2017
Title MELODI: Semantic Similarity of Words and Compositional Phrases using Latent Vector Weighting
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

In this paper we present our system for the SemEval 2013 Task 5a on semantic similar- ity of words and compositional phrases. Our system uses a dependency-based vector space model, in combination with a technique called latent vector weighting. The system computes the similarity between a particular noun in- stance and the head noun of a particular noun phrase, which was weighted according to the semantics of the modifier. The system is en- tirely unsupervised; one single parameter, the similarity threshold, was tuned using the train- ing data.