# KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification

arXiv: https://arxiv.org/abs/2002.10903 · HTML: https://mlsanity.com/paper/2002.10903/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Evaluation of Semantic Relation Knowledge of Pretrained Language Models and Humans](https://mlsanity.com/paper/2412.01131/) (2024)
- [SememeLM: A Sememe Knowledge Enhanced Method for Long-tail Relation Representation](https://mlsanity.com/paper/2406.10297/) (2024)
- [Inferring Adjective Hypernyms with Language Models to Increase the Connectivity of Open English Wordnet](https://mlsanity.com/paper/2506.10715/) (2025)

## Abstract (arXiv)

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.