Incorporating Relation Paths in Neural Relation Extraction
Table 3: F1 score for long tail situation.
['[EMPTY]', '[ITALIC] Ns≤1', '[ITALIC] Ns≤2', '[ITALIC] Ns≤5', 'All']
We evaluate the models on different parts of the long-tail distribution. To get testing instances from different parts of the distribution, we extract all the triple facts appearing less than Ns sentences in the testing set, and those sentences associating with them. All text related to ’ No Relation’ entity pair are also reserved in order to simulate noise. (1) Incorporating relation paths is indeed effective in predicting relations, and our models have significant improvements compared with the baselines. (2) Path+max indeed has larger improvements over CNN+max when Ns is small, which is consistent with our previous expectation. Also notice that the gap between Path+rand and CNN+rand is relatively constant. This is due to the fact that both these methods only use one random sentence, regardless of how many sentences there are associating with an entity pair.