A Comparison of Windowless and Window-Based Computational Association Measures as Predictors of Syntagmatic Human Associations

Distance-based (windowless) word assocation measures have only very recently appeared in the NLP literature and their performance compared to existing windowed or frequency-based measures is largely unknown. We conduct a largescale empirical comparison of a variety of distance-based and frequency-based measures for the reproduction of syntagmatic human assocation norms. Overall, our results show an improvement in the predictive power of windowless over windowed measures. This provides support to some of the previously published theoretical advantages and makes windowless approaches a promising avenue to explore further. This study also serves as a first comparison of windowed methods across numerous human association datasets. During this comparison we also introduce some novel variations of window-based measures which perform as well as or better in the human association norm task than established measures.