Paper: Inferring Selectional Preferences from Part-Of-Speech N-grams

ACL ID E12-1038
Title Inferring Selectional Preferences from Part-Of-Speech N-grams
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We present the PONG method to compute selectional preferences using part-of-speech (POS) N-grams. From a corpus labeled with grammatical dependencies, PONG learns the distribution of word relations for each POS N-gram. From the much larger but unlabeled Google N-grams corpus, PONG learns the distribution of POS N-grams for a given pair of words. We derive the probability that one word has a given grammatical relation to the other. PONG estimates this probability by combining both distributions, whether or not either word occurs in the labeled corpus. PONG achieves higher average precision on 16 relations than a state-of-the-art baseline in a pseudo-disambiguation task, but lower coverage and recall.