The hidden Markov Topic model: a probabilistic model of semantic representation.
about
Statistical Inference in Hidden Markov Models Using k-Segment ConstraintsEncoding sequential information in semantic space models: comparing holographic reduced representation and random permutationAutomatic extraction of property norm-like data from large text corpora.Learning bundles of stimuli renders stimulus order as a cue, not a confoundTaxonomic and thematic semantic systems.Acquisition of abstract concepts is influenced by emotional valence.
P2860
The hidden Markov Topic model: a probabilistic model of semantic representation.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
2010年论文
@zh
2010年论文
@zh-cn
name
The hidden Markov Topic model: a probabilistic model of semantic representation.
@en
type
label
The hidden Markov Topic model: a probabilistic model of semantic representation.
@en
prefLabel
The hidden Markov Topic model: a probabilistic model of semantic representation.
@en
P1476
The hidden Markov Topic model: a probabilistic model of semantic representation
@en
P2093
Gabriella Vigliocco
Mark Andrews
P304
P356
10.1111/J.1756-8765.2009.01074.X
P577
2010-01-01T00:00:00Z