NaDiR: Naive Distributional Response Generation

Abstract

This paper describes NaDiR (Naive DIstributional Response generation), a corpus-based system that, from a set of word stimuli as an input, generates a response word relying on association strength and distributional similarity. NaDiR participated in the CogALex 2014 shared task on multiword associations (restricted systems track), operationalizing the task as a ranking problem: candidate words from a large vocabulary are ranked by their average association or similarity to a given set of stimuli. We also report on a number of experiments conducted on the shared task data, comparing first-order models (based on co-occurrence and statistical association) to second-order models (based on distributional similarity).