Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts
Table 4: Performance of models on labeled (non-value) span prediction during cross-validation prior to decoding. We found using a CRF to be the most important aspect: simply using fixed word vectors with a CRF (i.e., the log-linear model) was sufficient to predict spans.
['Model', 'Span prediction P', 'Span prediction R', 'Span prediction  [ITALIC] F1']
With respect to span prediction Indeed, the log-linear baseline, which directly uses these word vectors as features for a CRF, did the best at span prediction. We believe that the drop in performance from introducing hidden layers with the neural models is a result of the model updating its span representations to do better edge prediction.