Extract Similarities from Syntactic Contexts: a Distributional Semantic Model Based on Syntactic Distance

Distributional Semantics (DS) models are based on the idea that two words which appear in similar contexts, i.e. similar neighborhoods, have similar meanings. This concept was originally presented by Harris in his Distributional Hypothesis (DH) (Harris 1954). Even though DH forms the basis of the majority of DS models, Harris states in later works that only syntactic analysis can allow for a more precise formulation of the neighborhoods involved: the arguments and the operators. In this work, we present a DS model based on the concept of Syntactic Distance inspired by a study of Harris’s theories concerning the syntactic-semantic interface. In our model, the context of each word is derived from its dependency network generated by a parser. With this strategy, the co-occurring terms of a target word are calculated on the basis of their syntactic relations, which are also preserved in the event of syntactical transformations. The model, named Syntactic Distance as Word Window (SD-W2), has been tested on three state-of-the-art tasks: Semantic Distance, Synonymy and Single Word Priming, and compared with other classical DS models. In addition, the model has been subjected to a new test based on Operator-Argument selection. Although the results obtained by SD-W2 do not always reach those of modern contextualized models, they are often above average and, in many cases, they are comparable with the result of GLOVE or BER