Investigating Unstructured Texts with Latent Semantic Analysis Export

@incollection{citeulike:5726919, abstract = {Latent semantic analysis (LSA) is an algorithm applied to approximate the meaning of texts, thereby exposing semantic structure to computation. LSA combines the classical vector-space model — well known in computational linguistics — with a singular value decomposition (SVD), a two-mode factor analysis. Thus, bag-of-words representations of texts can be mapped into a modified vector space that is assumed to reflect semantic structure. In this contribution the authors describe thelsapackage for the statistical language and environment R and illustrate its proper use through examples from the areas of automated essay scoring and knowledge representation.}, author = {Wild, Fridolin and Stahl, Christina}, citeulike-article-id = {5726919}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-70981-7_43}, citeulike-linkout-1 = {http://www.springerlink.com/content/g7u377132gq5623g}, doi = {10.1007/978-3-540-70981-7_43}, journal = {Advances in Data Analysis}, keywords = {diss}, pages = {383--390}, posted-at = {2009-09-05 23:50:35}, priority = {2}, title = {Investigating Unstructured Texts with Latent Semantic Analysis}, url = {http://dx.doi.org/10.1007/978-3-540-70981-7_43}, year = {2007} }

TY - CHAP ID - citeulike:5726919 L3 - citeulike-article-id:5726919 N2 - Latent semantic analysis (LSA) is an algorithm applied to approximate the meaning of texts, thereby exposing semantic structure to computation. LSA combines the classical vector-space model — well known in computational linguistics — with a singular value decomposition (SVD), a two-mode factor analysis. Thus, bag-of-words representations of texts can be mapped into a modified vector space that is assumed to reflect semantic structure. In this contribution the authors describe thelsapackage for the statistical language and environment R and illustrate its proper use through examples from the areas of automated essay scoring and knowledge representation. JF - Advances in Data Analysis EP - 390 TI - Investigating Unstructured Texts with Latent Semantic Analysis SP - 383 KW - diss AU - Wild, Fridolin AU - Stahl, Christina PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-70981-7_43 ER -