Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Table 4: Segment recall from WSJ separated by phrase type. The 10 most frequent phrase types are shown above, and the highest value in each row is bolded. P-UP=PRNP-UP, P-LM=PRPN-LM
['Label', 'Count', 'DIORA', 'P-UP', 'P-LM']
In many scenarios, one is only concerned with extracting particular constituent phrases rather than a full parse. Common use cases would be identifying entities, noun phrases, or verb phrases for downstream analysis. To get an idea of how well our model can perform on phrase segmentation, we consider the maximum recall of spans in our predicted parse tree. We leave methods for cutting the tree to future work and instead consider the maximum recall of our model which serves as an upper bound on its performance. Recall here is the percentage of labeled constituents that appear in our predicted tree relative to the total number of constituents in the gold tree. DIORA achieves the highest recall across the most types and is the only model to perform effectively on verb-phrases. Interestingly, DIORA performs worse than PRPN-LM at prepositional phrases.