An Empirical Evaluation of Dimensionality Reduction Using Latent Semantic Analysis on Hindi Text

Dimensionality reduction is the process of deriving an approximate representation of a dataset, that can reflect most of the correlations underlying within the dataset. In the context of text processing, dimensionality reduction is used for transforming any text to a precise representation that efficiently identifies the main insights of the original text. LSA(Latent Semantic Analysis) is a technique that is used to find correlations between words and sentences based on the usage of words within the text. This paper addresses the issue of dimensionality reduction in representing relevant data from Hindi text using LSA. An empirical evaluation is performed to find the influence of language complexity and influence of various weighting schemes on dimensionality reduction. The results are presented using the standard measures such as recall, precision and F-score.