Exploiting Linguistic Features for Sentence Completion

This paper presents a novel approach to automated sentence completion based on pointwise mutual information (PMI). Feature sets are created by fusing the various types of input provided to other classes of language models, ultimately allowing multiple sources of both local and distant information to be considered. Furthermore, it is shown that additional precision gains may be achieved by incorporating feature sets of higher-order n-grams. Experimental results demonstrate that the PMI model outperforms all prior models and establishes a new state-of-the-art result on the Microsoft Research Sentence Completion Challenge.