  Composition models of distributional semantics are used to construct phrase
representations from the representations of their words. Composition models are
typically situated on two ends of a spectrum. They either have a small number
of parameters but compose all phrases in the same way, or they perform
word-specific compositions at the cost of a far larger number of parameters. In
this paper we propose transformation weighting (TransWeight), a composition
model that consistently outperforms existing models on nominal compounds,
adjective-noun phrases and adverb-adjective phrases in English, German and
Dutch. TransWeight drastically reduces the number of parameters needed compared
to the best model in the literature by composing similar words in the same way.
