Machine Reading with Background Knowledge
Table 6: PPAD vs. baselines.
['[EMPTY]', 'PPAD', 'PPAD-', 'Coll-', 'Stan-']
For each dataset, we also show results when the “of” quads are removed, shown as “WKP\of”, “NYTC\of”, and “WSJ\of”. Our method yields improvements over the baselines. Improvements are especially significant on the datasets for which no labeled data was available (NYTC and WKP). On WKP, our method is 7% and 9% ahead of the Collins baseline and the Stanford parser, respectively. On NYTC, our method is 4% and 6% ahead of the Collins baseline and the Stanford parser, respectively. On WSJ, which is the source of the labeled data, our method is not significantly better than the Collins baseline. We could not evaluate the Stanford parser on the WSJ dataset. The parser requires well-formed sentences which we could not generate from the WSJ dataset as it was only available to us in the form of PP quads with no other sentence information. For the same reason, we could not generate discourse features,F7, for the WSJ PP quads. For the NYTC and WKP datasets, we generated well-formed short sentences containing only the PP quad and the noun preceding it.