Paper: Exploring Vector Space Models to Predict the Compositionality of German Noun-Noun Compounds

ACL ID S13-1038
Title Exploring Vector Space Models to Predict the Compositionality of German Noun-Noun Compounds
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper explores two hypotheses regarding vector space models that predict the compo- sitionality of German noun-noun compounds: (1) Against our intuition, we demonstrate that window-based rather than syntax-based distri- butional features perform better predictions, and that not adjectives or verbs but nouns rep- resent the most salient part-of-speech. Our overall best result is state-of-the-art, reach- ing Spearman?s ? = 0.65 with a word- space model of nominal features from a 20- word window of a 1.5 billion word web cor- pus. (2) While there are no significant dif- ferences in predicting compound?modifier vs. compound?head ratings on compositionality, we show that the modifier (rather than the head) properties predominantly influence the degree of compositionality of the compound.