id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_artm7eh6h5gdbnh3vbgfg4fcga	Jing Wang	A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment	2015	14	.pdf	application/pdf	8550	896	71	One approach would be to run LDA on the instances for an ambiguous word, then simply interpret topics as induced senses (Brody and Lapata, target word instance, where the sense labels are combination of separate LDA models based on different feature sets (e.g. word tokens, parts of speech, work in that we model sense and topic as two separate latent variables and learn them jointly. context word, we sample a new topic/sense pair for it over topics for global context word token i in instance d as Pr(t(i)g = j|d,t−i,s, ·), where t(i)g = j topic/sense pairs for a local context word token w(i)' of times sense k and topic j are assigned to some local word tokens. This distribution is considered the final sense assignment distribution for the target word in instance d for generate each word from a topic or sense, with a Topic modelling-based word sense induction.	./cache/work_artm7eh6h5gdbnh3vbgfg4fcga.pdf	./txt/work_artm7eh6h5gdbnh3vbgfg4fcga.txt