In this way, we generate dependency-based features for context words such as seee "see" in (seee, flm, +2) (based on the target word flm in the context of flm to seee).
expanded dependency features may introduce noise, and we therefore introduce expanded dependency weights w d ∈ {0.0, 0.5, 1.0} to ameliorate the effects of noise: a weight of w d = 0.0 means no expansion, while 1.0 means expanded dependencies are indistinguishable from non-expanded (strict match) dependencies.