Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization
Lucie Flek, Iryna Gurevych · 2016
Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation.To date, no effort has been put into integrating the supersenses into distributional word representations.We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and supersenses in the same vector space.Using these embeddings in a deep neural network model, we demonstrate that the supersense enrichment leads to a significant improvement in a range of downstream classification tasks.