Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
Table 4: Relation extraction results.
['[EMPTY]', 'P', 'Disease R', 'F1', 'P', 'Drug R', 'F1']
We evaluate the performance of different systems from an IR perspective: a title entity (i.e., document name) and a relation together act as a query, and the extracted NPs as retrieval results. The systems with “ *” are directly tuned on the evaluation data. Other systems are tuned with a tuning dataset. For the disease domain, D-Learner achieves its best performance without using any examples from the coFailure constraint, while for the drug domain, it achieves its best performance without using any examples from docFailure, secFailure, and titleFailure constraints. The tuning details will be discussed later.