Modelling metaphor with attribute-based semantics Modelling metaphor with attribute-based semantics
Paper summary They propose using attribute-based vectors for detecting metaphorical word pairs. Traditional embeddings (word2vec and count-based) are mapped to attribute vectors, using a supervised system trained on McRae norms. These vectors for a word pair are then given as input to an SVM classifier and trained to detect metaphorical (black humour) vs literal (black dress) word pairs. They show that using the attribute vectors gives higher F score over using the original vector space.

Summary by Marek Rei 3 years ago
Your comment: allows researchers to publish paper summaries that are voted on and ranked!

Sponsored by: and