  OmniGraph, a novel representation to support a range of NLP classification
tasks, integrates lexical items, syntactic dependencies and frame semantic
parses into graphs. Feature engineering is folded into the learning through
convolution graph kernel learning to explore different extents of the graph. A
high-dimensional space of features includes individual nodes as well as complex
subgraphs. In experiments on a text-forecasting problem that predicts stock
price change from news for company mentions, OmniGraph beats several benchmarks
based on bag-of-words, syntactic dependencies, and semantic trees. The highly
expressive features OmniGraph discovers provide insights into the semantics
across distinct market sectors. To demonstrate the method's generality, we also
report its high performance results on a fine-grained sentiment corpus.
