id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_weg2bzj74zc7ho3i3aprp32l5e	Ke Zhai	Online Adaptor Grammars with Hybrid Inference	2014	12	.pdf	application/pdf	7874	1246	68	Past variational inference techniques for adaptor grammars assume a preprocessing step that We show our approach's scalability and effectiveness by applying our inference framework in Section 5 on two tasks: unsupervised word segmentation and infinite-vocabulary topic modeling. Probabilistic context-free grammars (PCFG) define probability distributions over derivations of a Variational inference, however, is inherently parallel and easily amendable to online inference, but requires preprocessing to discover the adapted productions. The adaptor grammar inference methods use an approximate PCFG to equivalent to creating a "new table" in MCMC inference and provides truncation-free variational updates (Wang and Blei, 2012) by sampling a unseen Our approach is based on the stochastic variational inference for topic models (Hoffman et al., Algorithm 2 Online inference for adaptor grammars inference and topic models violate a fundamental assumption in online algorithms: new words are introduced as more data are streamed to the algorithm.	./cache/work_weg2bzj74zc7ho3i3aprp32l5e.pdf	./txt/work_weg2bzj74zc7ho3i3aprp32l5e.txt