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.
Flekova and Gurevych REF integrated supersense into distributional word representation, and trained supersense embedding and word embedding in the same vector space.
Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization
ACL
Computer Science