University of Tartu Faculty of Mathematics and Informatics
نویسنده
چکیده
ion level fp-rate fn-rate fp-rate treshold Figure 3.2: The generalization steps when mining fp-safe patterns with Algorithm 1. Blue dots represent maximal patterns, gray dots are infrequent (too generic) patterns, white dots are frequent, but not maximal patterns. Arrows depict the generalization DAG of the seed patterns, where solid ones show the actual search tree and dashed ones the edges that will not be traversed. consider only closed patterns: Cfp(τ) = Pfp(τ) \ {p : ∃q p : fp(q) = fp(p)} , (3.7) Cfn(τ) = Pfn(τ) \ {p : ∃q ≺ p : tp(q) = tp(p)} . (3.8) Of course, the claim is true only if we indeed have completely labeled corpus. Otherwise, we can only approximate fp(p) and tp(p) and thus closed patterns under incomplete labeling do not correspond to closed patterns under complete labeling. 3.2 Algorithm for finding fp-safe patterns To traverse all fp-safe patterns we have to start from a list of seed patterns and then traverse the lattice of all patterns by generalizing patterns until the rate of false positives goes over the threshold τ . To evaluate fp measure, we have to fix a corpus consisting of negative examples—set of sentences that do not encode the relation we seek. As a first approximation, we can take a
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