A Generalisation of Lexical Functions for Composition in Distributional Semantics

Abstract

Over the last two decades, numerous algorithms have been developed that successfully capture something of the semanticsof single words by looking at their distribution in text and comparing these distributions in a vector space model. However, it is not straightforward to construct meaning representations beyond the levelof individual words–i.e. the combination of words into larger units – using dis-tributional methods. Our contribution is twofold. First of all, we carry out a large-scale evaluation, comparing different composition methods within the distributional framework for the cases of both adjective-noun and noun-noun composition, makinguse of a newly developed dataset. Secondly, we propose a novel method focomposition, which generalises the approach by Baroni and Zamparelli (2010). The performance of our novel method is also evaluated on our new dataset andproves competitive with the best methods.