Lexical Semantic Recognition
Table 2: STREUSLE test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. −Lexcat: excluding lexical category. −SS: excluding supersense. Labeled F: labeled identification F1-score. SNACS: preposition supersenses. MWE LinkAvg P, R, F: evaluates MWE identification with partial credit. Schneider et al. (2018b): previous best tagger for the full SNACS task, reported on a slightly older version of STREUSLE.
['[EMPTY]', '[BOLD] Tags  [BOLD] Full', '[BOLD] Tags − [BOLD] Lexcat', '[BOLD] Tags − [BOLD] SS', '[BOLD] All Strong Expr. (incl. no supersense)  [BOLD] ID', '[BOLD] All Strong Expr. (incl. no supersense)  [BOLD] ID', '[BOLD] All Strong Expr. (incl. no supersense)  [BOLD] ID', '[BOLD] All Strong Expr. (incl. no supersense)  [BOLD] Labeled', '[BOLD] All Strong Expr. (incl. no supersense)  [BOLD] Labeled', '[BOLD] All Strong Expr. (incl. no supersense)  [BOLD] Labeled', '[BOLD] NOUN  [BOLD] Labeled', '[BOLD] VERB  [BOLD] Labeled', '[BOLD] SNACS  [BOLD] Labeled', '[BOLD] SNACS  [BOLD] Role', '[BOLD] SNACS  [BOLD] Fxn', '[BOLD] MWE  [BOLD] LinkAvg', '[BOLD] MWE  [BOLD] LinkAvg', '[BOLD] MWE  [BOLD] LinkAvg']
For preposition supersenses (SNACS), we compare to the results in Schneider et al. Note that Schneider et al. However, our baseline tagger, even with GloVe embeddings, outperforms Schneider et al. Using BERT embeddings with predicted POS tags and lemmas improves performance substantially; on preposition supersense tagging, it even outperforms using gold POS tags and lemmas. Liu et al.