A97-1025 Bayesian classifier . 2 Related Work Latent Semantic Analysis has been applied to the problem
A97-1025 % for this confusion set . 5.2 Latent Semantic Analysis Table 2 shows the performance
C02-1072 analysis ( LSA ) and probabilistic latent semantic analysis ( PLSA ) are applied for the
C04-1167 called syntactically enhanced latent semantic analysis and its application in statistical
A97-1025 Contextual Spelling Correction Using Latent Semantic Analysis </title> P Jones H Martin Abstract
D09-1049 contextual information in the form of Latent Semantic Analysis ( LSA ) vectors . LSA vectors
C04-1167 and Jurafsky , 1998 ) , where latent semantic analysis ( LSA ) ( Landauer et al. , 1998
A00-1013 in combination with tools like Latent Semantic Analysis ( LSA , Landauer &amp; Dumais
A97-1025 . In this paper , we introduce Latent Semantic Analysis ( LSA ) as a method for correcting
D09-1033 measures ) . Other approaches use Latent Semantic Analysis ( LSA ) to determine the similarity
A97-1025 sentence . We explore the use of Latent Semantic Analysis for correcting these incorrectly
D09-1045 statistical foundation . Probabilistic Latent Semantic Analysis ( pLSA , Hofmann ( 2001 ) ) casts
C04-1145 Mutual Information ) and SO-LSA ( Latent Semantic Analysis ) . SO-PMI will be our focus
C04-1167 termed as syntactically enhanced latent semantic analysis ( SELSA ) . This approach can
C04-1167 helped recently is the use of latent semantic analysis to capture the semantic fabric
D09-1091 spaces . Spectral method such as Latent Semantic Analysis has been commonly applied for
C04-1152 ( 2000 ) extend this by using latent semantic analysis ( Dumais et al. , 1988 ) to require
D09-1083 vector construction methods such as Latent Semantic Analysis ( Deer - wester et al. , 1990
C02-1061 the LSI model ( DDL +90 ) from latent semantic analysis ( LSA ) studies in psycholinguistics
C02-1072 human-knowledge in acquiring information , Latent Semantic Analysis ( LSA ) andProbabilisticLSA (
hide detail