scholarly journals Using Latent Semantic Analysis to Score Short Answer Constructed Responses: Automated Scoring of the Consequences Test

2019 ◽  
Vol 80 (2) ◽  
pp. 399-414
Author(s):  
Noelle LaVoie ◽  
James Parker ◽  
Peter J. Legree ◽  
Sharon Ardison ◽  
Robert N. Kilcullen
Keyword(s):  
Latent Semantic Analysis ◽  
Divergent Thinking ◽  
Semantic Analysis ◽  
Performance Criteria ◽  
Learning Approaches ◽  
Automated Scoring ◽  
Short Answer ◽  
Wide Range ◽  
Constructed Responses ◽  
Highly Correlated

Automated scoring based on Latent Semantic Analysis (LSA) has been successfully used to score essays and constrained short answer responses. Scoring tests that capture open-ended, short answer responses poses some challenges for machine learning approaches. We used LSA techniques to score short answer responses to the Consequences Test, a measure of creativity and divergent thinking that encourages a wide range of potential responses. Analyses demonstrated that the LSA scores were highly correlated with conventional Consequence Test scores, reaching a correlation of .94 with human raters and were moderately correlated with performance criteria. This approach to scoring short answer constructed responses solves many practical problems including the time for humans to rate open-ended responses and the difficulty in achieving reliable scoring.

scholarly journals Automated Scoring of Handwritten Essays Based on Latent Semantic Analysis

2006 ◽  
pp. 71-83 ◽  
Author(s):  
Sargur Srihari ◽  
Jim Collins ◽  
Rohini Srihari ◽  
Pavithra Babu ◽  
Harish Srinivasan
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Automated Scoring

Identifying Latent Semantics in Action Games for Player Modeling

2019 ◽  
Vol 11 (2) ◽  
pp. 1-21
Author(s):  
Katia Lida Kermanidis
Keyword(s):  
Machine Learning ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Learning Approaches ◽  
Hidden Information ◽  
Game Semantics ◽  
Knowledge Based ◽  
Behavioral Features ◽  
Player Modeling ◽  
High Level

Machine learning approaches to player modeling traditionally employ a high-level game-knowledge-based feature for representing game sessions, and often player behavioral features as well. The present work makes use of generic low-level features and latent semantic analysis for unsupervised player modeling, but mostly for revealing underlying hidden information regarding game semantics that is not easily detectable beforehand.

Application of Latent Semantic Analysis to Divergent Thinking is Biased by Elaboration

2018 ◽  
Vol 53 (4) ◽  
pp. 559-575 ◽  
Author(s):  
Boris Forthmann ◽  
Oluwatosin Oyebade ◽  
Adebusola Ojo ◽  
Fritz Günther ◽  
Heinz Holling
Keyword(s):  
Latent Semantic Analysis ◽  
Divergent Thinking ◽  
Semantic Analysis

A hybrid machine learning approach of fuzzy-rough-k-nearest neighbor, latent semantic analysis, and ranker search for efficient disease diagnosis

2021 ◽  
pp. 1-16
Author(s):  
Sunil Kumar Jha ◽  
Ninoslav Marina ◽  
Jinwei Wang ◽  
Zulfiqar Ahmad
Keyword(s):  
Machine Learning ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Nearest Neighbor ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Machine Learning Approach ◽  
Hybrid Machine

Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively.

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.

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