KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification
Table 2: LRC results for each lexical relation types over the CogALex-V shared task in terms of F1.
['[BOLD] Method', 'SYN', 'ANT', 'HYP', 'MER', 'All']
We evaluate KEML over the CogALex-V Shared Task (Subtask 2) Santus et al. This dataset is most challenging as it contains a large number of random word pairs and disables “lexical memorization”. The organizer requires participants to discard the results of the random class from average, and report the F1 scores for each type of lexical relations. We consider two top systems reported in this task (i.e., GHHH Attia et al. as strong competitors. It also has highest scores on three types of lexical relations: synonymy (SYN), hypernymy (HYP) and meronymy (MER).