Dynamic and Static Prototype Vectors for Semantic Composition

Compositional Distributional Semantic methods model the distributional behavior of a compound word by exploiting the distributional behavior of its constituent words. In this setting, a constituent word is typically represented by a feature vector conflating all the senses of that word. However, not all the senses of a constituent word are relevant when composing the semantics of the compound. In this paper, we present two different methods for selecting the relevant senses of constituent words. The first one is based on Word Sense Induction and creates a static multi prototype vectors representing the senses of a constituent word. The second creates a single dynamic prototype vector for each constituent word based on the distributional properties of the other constituents in the compound. We use these prototype vectors for composing the semantics of noun-noun compounds and evaluate on a compositionality-based similarity task. Our results show that: (1) selecting relevant senses of the constituent words leads to a better semantic composition of the compound, and (2) dynamic prototypes perform better than static prototypes.