scholarly journals A Comparison between Factor Structure and Semantic Representation of Personality Test Items Using Latent Semantic Analysis

2019 ◽  
Vol 30 (3) ◽  
pp. 133-156
Author(s):  
Park Sungjoon ◽  
Cheongtag Kim ◽  
Hee Young Park
Keyword(s):  
Factor Structure ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Semantic Representation ◽  
Personality Test ◽  
Test Items

scholarly journals Performance Evaluation of LSA, NMF and ILSA in Electronic Assessment of Free Text Document

2021 ◽  
pp. 46-56
Author(s):  
M. M. Rufai ◽  
A. O. Afolabi ◽  
O. D. Fenwa ◽  
F. A. Ajala
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Semantic Representation ◽  
Free Text ◽  
Test Question ◽  
Text Document ◽  
Electronic Assessment ◽  
Term Similarity ◽  
F Measure ◽  
Improved Algorithm

Aims: To evaluate the performance of an Improved Latent Semantic Analysis (ILSA), Latent Semantic Analysis (LSA), Non-Negative Matrix Factorization (NMF) algorithms in an Electronic Assessment Application using metrics, Term Similarity, Precision, Recall and F-measure functions, Mean divergence, Assessment Accuracy and Adequacy in Semantic Representation. Methodology: The three algorithms were separately applied in developing an Electronic Assessment application. One hundred students’ responses to a test question in an introductory artificial intelligence course were used. Their performance was measured based on the following metrics, Term Similarity, Precision, Recall and F-measure functions, Mean divergence and Assessment Accuracy. Results: ILSA outperformed the LSA and NMF with an assessment accuracy of 96.64, mean divergence from manual score of 0.03, and recall, precision and f-measure value of 0.83, 0.85 and 0.87 respectively. Conclusion: The research observed the performance of an improved algorithm ILSA for electronic Assessment of free text document using Adequacy in Semantic Representation, Retrieval Quality and Assessment Accuracy as performance metrics. The results obtained from the experimental designs shows the adequacy of the improved algorithm in semantic representation, better retrieval quality and improved assessment accuracy.

Summary Street®: Computer Support for Comprehension and Writing

2005 ◽  
Vol 33 (1) ◽  
pp. 53-80 ◽  
Author(s):  
Marita Franzke ◽  
Eileen Kintsch ◽  
Donna Caccamise ◽  
Nina Johnson ◽  
Scott Dooley
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Computer Support ◽  
Control Group ◽  
Automatic Evaluation ◽  
Writing Quality ◽  
Summary Writing ◽  
Test Items ◽  
8Th Grade ◽  
New Material

Having students express their understanding of difficult, new material in their own words is an effective method to deepen their comprehension and learning. Summary Street® is a computer tutor that offers a supportive context for students to practice this activity by means of summary writing, guiding them through successive cycles of revising with feedback on the content of their writing. Automatic evaluation of the content of student summaries is enabled by Latent Semantic Analysis (LSA). This article describes an experimental study of the comprehension and writing tutor, in which 8th-grade students practiced summary writing over a 4-week period, either with or without the guidance of the tutor. Students using Summary Street® scored significantly higher on an independent comprehension test than the control group for test items that tapped gist level comprehension. Their summaries were also judged to be significantly superior in blind scoring on several measures of writing quality. Students of low-to-moderate achievement levels benefitted most from the tool.

Task Estimation Using Latent Semantic Analysis of Visual Scenes and Spoken Words

2012 ◽  
Vol 132 (9) ◽  
pp. 1473-1480
Author(s):  
Masashi Kimura ◽  
Shinta Sawada ◽  
Yurie Iribe ◽  
Kouichi Katsurada ◽  
Tsuneo Nitta
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Spoken Words ◽  
Visual Scenes

Similarity Detection Using Latent Semantic Analysis Algorithm

2018 ◽  
Vol 6 (8) ◽  
pp. 102
Author(s):  
Priyanka R. Patil ◽  
Shital A. Patil
Keyword(s):  
Latent Semantic Analysis ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Mining Method ◽  
Research Papers ◽  
Information Measures ◽  
Automated Software ◽  
Day By Day ◽  
Ways Of Life ◽  
Dirichlet Allocation

Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. LSA with synonym rate of accuracy is greater when the synonym are consider for matching.

LATENT-SEMANTIC ANALYSIS, SOCIAL NETWORKS AND NON-STRUCTURED DATA: INTERACTION METHOD

2019 ◽  
Keyword(s):  
Social Networks ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Statistical Processing ◽  
Unstructured Data ◽  
Average Score ◽  
Text Type ◽  
Internet Users ◽  
The Matrix ◽  
Human Thinking

This article examines the method of latent-semantic analysis, its advantages, disadvantages, and the possibility of further transformation for use in arrays of unstructured data, which make up most of the information that Internet users deal with. To extract context-dependent word meanings through the statistical processing of large sets of textual data, an LSA method is used, based on operations with numeric matrices of the word-text type, the rows of which correspond to words, and the columns of text units to texts. The integration of words into themes and the representation of text units in the theme space is accomplished by applying one of the matrix expansions to the matrix data: singular decomposition or factorization of nonnegative matrices. The results of LSA studies have shown that the content of the similarity of words and text is obtained in such a way that the results obtained closely coincide with human thinking. Based on the methods described above, the author has developed and proposed a new way of finding semantic links between unstructured data, namely, information on social networks. The method is based on latent-semantic and frequency analyzes and involves processing the search result received, splitting each remaining text (post) into separate words, each of which takes the round in n words right and left, counting the number of occurrences of each term, working with a pre-created semantic resource (dictionary, ontology, RDF schema, ...). The developed method and algorithm have been tested on six well-known social networks, the interaction of which occurs through the ARI of the respective social networks. The average score for author's results exceeded that of their own social network search. The results obtained in the course of this dissertation can be used in the development of recommendation, search and other systems related to the search, rubrication and filtering of information.

Web clustering based on hybrid probabilistic latent semantic analysis model

2013 ◽  
Vol 32 (11) ◽  
pp. 3018-3022 ◽  
Author(s):  
Zhi-he WANG ◽  
Ling-yun WANG ◽  
Hui DANG ◽  
Li-na PAN
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Probabilistic Latent Semantic Analysis ◽  
Analysis Model ◽  
Web Clustering
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