  Lexical relations describe how concepts are semantically related, in the form
of relation triples. The accurate prediction of lexical relations between
concepts is challenging, due to the sparsity of patterns indicating the
existence of such relations. We propose the Knowledge-Enriched Meta-Learning
(KEML) framework to address the task of lexical relation classification. In
KEML, the LKB-BERT (Lexical Knowledge Base-BERT) model is presented to learn
concept representations from massive text corpora, with rich lexical knowledge
injected by distant supervision. A probabilistic distribution of auxiliary
tasks is defined to increase the model's ability to recognize different types
of lexical relations. We further combine a meta-learning process over the
auxiliary task distribution and supervised learning to train the neural lexical
relation classifier. Experiments over multiple datasets show that KEML
outperforms state-of-the-art methods.
