Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings
TABLE II: Multilingual word similarity.
['[BOLD] Method  [BOLD] Dataset', '[BOLD] English  [BOLD] WS-353', '[BOLD] English  [BOLD] SimLex', '[BOLD] English  [BOLD] RW-Frequent', '[BOLD] English  [BOLD] RW-OOV', '[BOLD] German  [BOLD] WS-353', '[BOLD] German  [BOLD] SimLex', '[BOLD] Turkish  [BOLD] AnlamVer']
Additionally, methods that discriminately generate these morphemes outperform FastText that indiscriminately generate all subwords. Finally, while most subword enriched embeddings perform well on word similarity, MorphMine shines particularly in the similarity task on rare words where it outperforms all baselines. This is likely because MorphMine generates morphemes at multiple granularity which is more likely semantically link a rare word to a frequent word via a semantically-meaningful morpheme. This performance gap is even higher for out-of-vocabulary words were MorphMine significantly outperforms all other baselines at the word similarity task.