Paper: Parameter Estimation for LDA-Frames

ACL ID N13-1051
Title Parameter Estimation for LDA-Frames
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

LDA-frames is an unsupervised approach for identifying semantic frames from semanti- cally unlabeled text corpora, and seems to be a useful competitor for manually created databases of selectional preferences. The most limiting property of the algorithm is such that the number of frames and roles must be pre- defined. In this paper we present a modifi- cation of the LDA-frames algorithm allowing the number of frames and roles to be deter- mined automatically, based on the character and size of training data.