Paper Info
Title
Inferring selectional preferences from part-of-speech N-grams
Abstract
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.
Year
Venue
Keywords
2012
EACL
unlabeled google n-grams corpus,grammatical dependency,part-of-speech n-grams,grammatical relation,pseudo-disambiguation task,pos n-grams,lower coverage,selectional preference,word relation,pos n-gram,higher average precision,pong method
Field
DocType
Citations 
Grammatical relation,Computer science,Part of speech,Speech recognition,Artificial intelligence,Natural language processing,Recall,Machine learning
Conference
2
PageRank 
References 
Authors
0.36
10
2
Name
Order
Citations
PageRank
Hyeju Jang1496.87
Jack Mostow21133263.51