Aggregate Extraction Let A6 be the set of extracted relations for any of the systems; we compute aggregate precision and recall by comparing A6 with A.

We then report precision and recall for each system on this set of sampled sentences.

Since the data contains an unbalanced number of instances of each relation, we also report precision and recall for each of the ten most frequent relations.

Table 1 presents this approximate precision and recall for MULTIR on each of the relations, along with statistics we computed to measure the quality of the weak supervision.

use supervised learning of relation-specific examples, which can achieve high precision and recall .

While they offer high precision and recall , these methods are unlikely to scale to the thousands of relations found in text on the Web.

We present the results in terms of F-score only for simplicity; we then conduct an error analysis that examines precision and recall .

We consider the performance in terms of precision and recall in addition to F-score — see Table 7 (a).

Overall, there is no major tradeoff between precision and recall across the different settings; although we can observe the following: (i) adding more training data helps precision more than recall (over three times more) — compare the last two columns in Table 7 (a); and (ii) the best setting has a slightly lower precision than all features, although a much better recall — compare columns 4 and 5 in Table 7 (a).

Both precision and recall are improved with two exceptions: recall of B3 decreases from line 2 to 3 and from 15 to 16.

In contrast to F1, there is no consistent trend for precision and recall .

But this higher variability for precision and recall is to be expected since every system trades the two measures off differently.

The resulting predictions improve the precision and recall of both alignment links and extraced phrase pairs in Chinese-English experiments.

The bidirectional model improves both precision and recall relative to all heuristic combination techniques, including grow-diag-final (Koehn et al., 2003).

As our model only provides small improvements in alignment precision and recall for the union combiner, the magnitude of the BLEU improvement is not surprising.

While the first level of constituent analysis has high precision and recall on NPs, the second level often does well finding prepositional phrases (PPS), especially in WSJ; see Table 7.

The table shows absolute improvement (+) or decline (—) in precision and recall when phrasal punctuation is removed from the data.

It measures precision and recall on constituents produced by a parser as compared to gold standard constituents.