Distinguishing Antonyms and Synonymsin a Pattern-based Neural Network
Table 5: Comparing pre-trained GloVe and dLCE word embeddings.
['[BOLD] Model', '[BOLD] Word Embeddings', '[BOLD] Adjective  [BOLD] P', '[BOLD] Adjective  [BOLD] R', '[BOLD] Adjective  [BOLD] F1', '[BOLD] Verb  [BOLD] P', '[BOLD] Verb  [BOLD] R', '[BOLD] Verb  [BOLD] F1', '[BOLD] Noun  [BOLD] P', '[BOLD] Noun  [BOLD] R', '[BOLD] Noun  [BOLD] F1']
The table shows that the dLCE word embeddings are better than the pre-trained GloVe word embeddings, by around .01 F1 for the pattern-based AntSynNET model and the combined AntSynNET model regarding adjective and verb pairs. Regarding noun pairs, the improvements of the dLCE word embeddings over pre-trained GloVe word embeddings achieve around .01 and .04 F1 for the pattern-based model and the combined model, respectively.