MATEC Web Conf.
Volume 189, 20182018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|Number of page(s)||6|
|Section||Cloud & Network|
|Published online||10 August 2018|
Analysis on the use of Latent Semantic Indexing (LSI) for document classification and retrieval system of PNP files
Technological Institute of the Philippines-Manila, Philippines
* Corresponding author: firstname.lastname@example.org
Document classification is the process of categorizing documents from many mixed files automatically . In this paper, an approach to classification of documents for admin-case files of Philippine National Police (PNP) using Latent Semantic Indexing (LSI) method is proposed. The model for this that represents term-to-term, document-todocument and term-to-document relationships has been applied. Regular Expression is implemented also to define a search pattern based on character strings which the LSI used to establish the semantic relevance of the character strings to the search term or keyword. The aim of the study is to evaluate the performance of LSI in classifying PNP documents; experimentation was done using software to test the capability of LSI towards text retrieval. Indexing is according to the pattern matched in the collection of text that uses model of SVD. Based on tests, documents were indexed based on file relationships and was able to return a search result as the retrieved information from PNP files. Weights are used to check the accuracy of the method; the positive values identified in query similarity are regarded as the most relevant among the related searches, meaning, the query word matches words in a text file and it returns a query result.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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