OmniGraph: Rich Representation and Graph Kernel Learning
Table 2: Mean accuracy for gfbf data experiments, where OmniGraph (in boldface) significantly outperforms the baseline and three benchmarks.
['[EMPTY]', 'Benef/Malef', 'WriterAttitude']
We use the percentage of the majority class as the baseline, and compare the same five methods as in our previous experiment. On the benefactive/malefactive task, BOW obtains a 10% improvement over the baseline. Structured representations significantly improve over BOW. SemTreeFWD, which incorporates the semantic frame features and a sentiment lexicon, improves the performance by another 5%. The dependency tree performance is similar to SemTreeFWD. OmniGraph with graph kernel learning (WL or NEW kernels) performs much better. Dependency trees and semantic trees do not improve over BOW. Both versions of OmniGraph, however, have superior performance.