A tensor-based factorization model of semantic compositionality

In this paper, we present a novel method for the computation of compositionality within a distri-butional framework. The key idea is that com-positionality is modeled as a multi-way interac-tion between latent factors, which are automat-ically constructed from corpus data. We use our method to model the composition of sub-ject verb object triples. The method consists of two steps. First, we compute a latent factor model for nouns from standard co-occurrence data. Next, the latent factors are used to induce a latent model of three-way subject verb object interactions. Our model has been evaluated on a similarity task for transitive phrases, in which it exceeds the state of the art.