Fusion of Compositional Network-based and Lexical Function Distributional Semantic Models

Distributional Semantic Models (DSMs) have been successful at modeling the meaning of individual words, with interest recently shifting to compositional structures, i.e., phrases and sentences. Network-based DSMs represent and handle semantics via operators applied on word neighborhoods, i.e., semantic graphs containing a target’s most similar words. We extend network-based DSMs to address compositionality using an activation model (motivated by psycholinguistics) that operates on the fused neighborhoods of variable size activation. The proposed method is evaluated against and combined with the lexical function method proposed by (Baroni and Zamparelli, 2010). We show that, by fusing a network-based with a lexical function model, performance gains can be achieved.