Insights into Analogy Completion from the Biomedical Domain
Table 3: MAP performance on the three BMASS relations with ≥100 unigram analogies. Uni is using unigram embeddings on unigram data, UniM is using MWE embeddings on unigram data, and MWE is performance with MWE embeddings over the full MWE data.
['Rel', 'PM-2 Uni', 'PM-2 Uni [ITALIC] M', 'PM-2 MWE', 'CBOW Uni', 'CBOW Uni [ITALIC] M', 'CBOW MWE']
Since prior work on analogies has primarily been concerned with unigram data, we also identified a subset of our data for which we could find single-word string realizations for all concepts in an analogy, using the full vocabulary of our trained embeddings. The unigram analogies are slightly better captured than the full MWE data for has-lab-number (L2) and has-tradename (L3); however, lower performance on the unigram subset in tradename-of (L4) shows that unigram analogies are not always easier. We see a small effect from the much larger set of candidate answers in the unigram case (>1m unigrams), as shown by the slightly higher MAP numbers in the UniM case. In general, it is clear that the difficulty of some of the relations in our dataset is not due solely to using MWEs in the analogies.