Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings
TABLE I: Morpheme Segmentation Performance.
['[BOLD] Dataset  [BOLD] Method', '[BOLD] English P', '[BOLD] English R', '[BOLD] English F1', '[BOLD] German P', '[BOLD] German R', '[BOLD] German F1', '[BOLD] Turkish P', '[BOLD] Turkish R', '[BOLD] Turkish F1']
As both BytePair Encoding and Unigram-LM require a morpheme vocabulary size parameter, for these methods, we perform a parameter sweep and report results from the highest performing run. Across all three languages, variants of MorphMine outperform with respect to F1 score. Further analysis shows this is primarily due to a higher recall. In comparison to MorphMine without global refinement, we see that implementing global refinement generally improves performance as seen in English and German and in the case of Turkish, performance between the MorphMine variants were overall comparable.