Jointly Embedding Entities and Text with Distant Supervision
Table 6: Accuracy % on 5 of the relations in BMASS with greatest absolute difference in word performance vs entity performance: B3 (gene-encodes-product), H1 (refers-to), C6 (associated-with), L1 (form-of), and L6 (has-free-acid-or-base-form). The better of word and entity performance is highlighted; all entity vs word differences are significant (McNemar’s test; p≪0.01).
['Method', 'B3', 'H1', 'C6', 'L1', 'L6']
For gene-encodes-product and refers-to, both of which require structured domain knowledge, entity embeddings significantly outperform word-level representations. Many of the errors made by word embeddings in these relations are due to lexical over-sensitivity: for example, in the renaming analogy spinal epidural hematoma:epidural hemorrhage::canis familiaris:dog, words suggest latinate completions such as latrans and caballus, while entities capture the correct C1280551 (dog). However, on more morphological relations such as has-free-acid-or-base-form, words are by far the better option.