Abstract
Syntax-based semantic spaces are more flexible and can potentially better model semantic relatedness than bag-of-words spaces. Their application is however limited by sparsity and restricted coverage. We address these problems by smoothing syntax-based with word-based spaces and investigate when to choose which prediction. We obtain the best results by picking the maximal predicted similarity for each word pair, taking advantage of the tendency of unreliable models to underestimate similarity. We show that smoothing can substantially improve coverage while maintaining prediction quality on two German benchmark tasks.
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