Local and Global Latent Semantic Analysis for Text Categorization

2018 ◽  
pp. 1360-1374 ◽  
Author(s):  
Khadoudja Ghanem
Keyword(s):  
Information Retrieval ◽  
High Precision ◽  
Latent Semantic Analysis ◽  
Classification Accuracy ◽  
Text Categorization ◽  
Semantic Analysis ◽  
Experimental Results ◽  
New Method ◽  
Semantic Approach ◽  
Document Categorization

In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.

Local and Global Latent Semantic Analysis for Text Categorization

2014 ◽  
Vol 4 (3) ◽  
pp. 1-13
Author(s):  
Khadoudja Ghanem
Keyword(s):  
Information Retrieval ◽  
High Precision ◽  
Latent Semantic Analysis ◽  
Classification Accuracy ◽  
Text Categorization ◽  
Semantic Analysis ◽  
Experimental Results ◽  
Semantic Approach ◽  
Document Categorization ◽  
Second Use

In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.

SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis

2014 ◽  
Vol 22 (2) ◽  
pp. 291-319 ◽  
Author(s):  
SHUDONG HAO ◽  
YANYAN XU ◽  
DENGFENG KE ◽  
KAILE SU ◽  
HENGLI PENG
Keyword(s):  
Latent Semantic Analysis ◽  
Scoring System ◽  
Semantic Analysis ◽  
Scoring Systems ◽  
Recall Rate ◽  
Experimental Results ◽  
Important Indicator ◽  
Simplified Chinese ◽  
Language Tests ◽  
Essay Scoring

AbstractWriting in language tests is regarded as an important indicator for assessing language skills of test takers. As Chinese language tests become popular, scoring a large number of essays becomes a heavy and expensive task for the organizers of these tests. In the past several years, some efforts have been made to develop automated simplified Chinese essay scoring systems, reducing both costs and evaluation time. In this paper, we introduce a system called SCESS (automated Simplified Chinese Essay Scoring System) based on Weighted Finite State Automata (WFSA) and using Incremental Latent Semantic Analysis (ILSA) to deal with a large number of essays. First, SCESS uses ann-gram language model to construct a WFSA to perform text pre-processing. At this stage, the system integrates a Confusing-Character Table, a Part-Of-Speech Table, beam search and heuristic search to perform automated word segmentation and correction of essays. Experimental results show that this pre-processing procedure is effective, with a Recall Rate of 88.50%, a Detection Precision of 92.31% and a Correction Precision of 88.46%. After text pre-processing, SCESS uses ILSA to perform automated essay scoring. We have carried out experiments to compare the ILSA method with the traditional LSA method on the corpora of essays from the MHK test (the Chinese proficiency test for minorities). Experimental results indicate that ILSA has a significant advantage over LSA, in terms of both running time and memory usage. Furthermore, experimental results also show that SCESS is quite effective with a scoring performance of 89.50%.

Latent Semantic Analysis and Beyond

2010 ◽  
pp. 546-570 ◽  
Author(s):  
Anne Kao
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Search Space ◽  
Latent Semantic Indexing ◽  
Semantic Indexing ◽  
Mathematical Approach ◽  
Text Information ◽  
Space Method

Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text mining. It is an extension of the vector space method in information retrieval, representing documents as numerical vectors but using a more sophisticated mathematical approach to characterize the essential features of the documents and reduce the number of features in the search space. This chapter summarizes several major approaches to this dimensionality reduction, each of which has strengths and weaknesses, and it describes recent breakthroughs and advances. It shows how the constructs and products of LSA applications can be made user-interpretable and reviews applications of LSA beyond information retrieval, in particular, to text information visualization.

Latent Semantic Analysis for Text Mining and Beyond

2013 ◽  
pp. 253-280 ◽  
Author(s):  
Anne Kao ◽  
Steve Poteet ◽  
Jason Wu ◽  
William Ferng ◽  
Rod Tjoelker ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Latent Semantic Analysis ◽  
Web Mining ◽  
Semantic Analysis ◽  
Search Space ◽  
Latent Semantic Indexing ◽  
Cross Language Information Retrieval ◽  
Text Information ◽  
Cross Language

Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text mining. It is an extension of the vector space method in information retrieval, representing documents as numerical vectors but using a more sophisticated mathematical approach to characterize the essential features of the documents and reduce the number of features in the search space. This chapter summarizes several major approaches to this dimensionality reduction, each of which has strengths and weaknesses, and it describes recent breakthroughs and advances. It shows how the constructs and products of LSA applications can be made user-interpretable and reviews applications of LSA beyond information retrieval, in particular, to text information visualization. While the major application of LSA is for text mining, it is also highly applicable to cross-language information retrieval, Web mining, and analysis of text transcribed from speech and textual information in video.

The Use of Text Mining Techniques in Electronic Discovery for Legal Matters

2012 ◽  
pp. 174-190
Author(s):  
Michael W. Berry ◽  
Reed Esau ◽  
Bruce Kiefer
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Matrix Factorization ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Electronic Documents ◽  
Collection Process ◽  
Relevance Judgments ◽  
Electronic Discovery ◽  
Non Negative Matrix Factorization

Electronic discovery (eDiscovery) is the process of collecting and analyzing electronic documents to determine their relevance to a legal matter. Office technology has advanced and eased the requirements necessary to create a document. As such, the volume of data has outgrown the manual processes previously used to make relevance judgments. Methods of text mining and information retrieval have been put to use in eDiscovery to help tame the volume of data; however, the results have been uneven. This chapter looks at the historical bias of the collection process. The authors examine how tools like classifiers, latent semantic analysis, and non-negative matrix factorization deal with nuances of the collection process.

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