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

Krishnamurthi, Karthik; Sudi, Ravi Kumar; Panuganti, Vijayapal Reddy; Vardhan, B. Vishnu

doi:10.1109/ialp.2013.11

Cited by 4 publications

(1 citation statement)

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“…LSA makes the machine clearly conceptualize the terms of the document by learning the context in which these terms are written. The maximum number of rows and columns used for input matrix data size in LSA based document collection analysis was 10783 × 600 [3]. LSA was unable to process the documents beyond this matrix size.…”

Section: Introductionmentioning

confidence: 99%

Disambiguating the Context of the Concept Terms using Concept Hierarchies

Dara*¹,

Reddy²

2019

IJITEE

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Latent Semantic Analysis (LSA) makes the machine clearly conceptualize the terms of the document by learning the context in which these terms are written. However, LSA suffers from the limitation of input data matrix size in terms of number of terms and number of documents of the considered dataset. When the size of the dataset is huge, LSA becomes inefficient towards learning the correct context and thereby is unable to produce the intended concepts by the machine. To overcome this problem, Context Disambiguation (ConDis) ontology is engineered for a domain which has the capability of evolving itself based on automatic learning of concepts and relations from the ever scaling documents over the web. The concept hierarchies from general to specific concepts combined with corresponding object relations specify the particular context for a term. These object relations based concept hierarchies clearly help disambiguate the context of the concept terms in an effective manner.

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Section: Introductionmentioning

confidence: 99%

Disambiguating the Context of the Concept Terms using Concept Hierarchies

Dara*¹,

Reddy²

2019

IJITEE

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An Efficient Hindi Text Classification Model Using SVM

Puri

Singh

2019

Lecture Notes in Networks and Systems

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Understanding Document Semantics from Summaries

Krishnamurthi

Panuganti²,

Vardhan

2016

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Summary of a document contains words that actually contribute to the semantics of the document. Latent Semantic Analysis (LSA) is a mathematical model that is used to understand document semantics by deriving a semantic structure based on patterns of word correlations in the document. When using LSA to capture semantics from summaries, it is observed that LSA performs quite well despite being completely independent of any external sources of semantics. However, LSA can be remodeled to enhance its capability to analyze correlations within texts. By taking advantage of the model being language independent, this article presents two stages of LSA remodeling to understand document semantics in the Indian context, specifically from Hindi text summaries. One stage of remodeling is done by providing supplementary information, such as document category and domain information. The second stage of remodeling is done by using a supervised term weighting measure in the process. The remodeled LSA’s performance is empirically evaluated in a document classification application by comparing the accuracies of classification to plain LSA. An improvement in the performance of LSA in the range of 4.7% to 6.2% is achieved from the remodel when compared to the plain model. The results suggest that summaries of documents efficiently capture the semantic structure of documents and is an alternative to full-length documents for understanding document semantics.

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

Cited by 4 publications

References 4 publications

Disambiguating the Context of the Concept Terms using Concept Hierarchies

Disambiguating the Context of the Concept Terms using Concept Hierarchies

An Efficient Hindi Text Classification Model Using SVM

Understanding Document Semantics from Summaries

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