In this section, we present our framework Sememe-Encoded WRL (SE-WRL) that considers sememe information for word sense disambiguation and representation learning.
we learn our models on a large-scale text corpus with the semantic regularization of the sememe annotation in HowNet and obtain sememe, sense and word embeddings for evaluation tasks.