Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model
Goran Glavaš, Ivan Vulić · 2018
We present a simple and effective feedforward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy).Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs.STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages.We also show that, if coupled with a lingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data.