| Abstract | We present a computational algorithm for estimating word maturity, based on modeling language acquisition with Latent Semantic Analysis. |
| Rethinking Word Difficulty | 3 Modeling Word Meaning Acquisition with Latent Semantic Analysis |
| Rethinking Word Difficulty | 3.1 Latent Semantic Analysis (LSA) |
| Rethinking Word Difficulty | An appealing choice for quantitatively modeling word meanings and their growth over time is Latent Semantic Analysis (LSA), an unsupervised method for representing word and document meaning in a multidimensional vector space. |
| Composite language model | Since only one pair of (d, w) is being observed, as a result, the joint probability model is a mixture of log-linear model with the expression p(d, w) = p(d) Zg p(wlg)p(9|d)- Typically, the number of documents and vocabulary size are much larger than the size of latent semantic class variables. |
| Composite language model | Thus, latent semantic class variables function as bottleneck variables to constrain word occurrences in |
| Introduction | (2006) integrated n-gram, structured language model (SLM) (Chelba and Jelinek, 2000) and probabilistic latent semantic analysis (PLSA) (Hofmann, 2001) under the directed MRF framework (Wang et al., 2005) and studied the stochastic properties for the composite language model. |