Performance Analysis of LSA for Descriptive Answer Assessment

Kaur, Amarjeet; Kumar, Mukesh

doi:10.1007/978-981-13-7082-3_8

Cited by 2 publications

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“…IEA was the first automated essay scoring system based on latent semantic analysis, a statistical analysis technique, which uses the analysis of essay content as an important reference indicator for scoring. e basic principle of IEA is derived from Latent Semantic Analysis (LSA) [14], a statistical method developed by the psychologist omas Landauer, which is a statistical calculation to extract the specific meaning of words and phrases in a given context. It starts by representing the different semantic units of a composition in a high-dimensional semantic space, each semantic unit being a point in this semantic space, and the semantic similarity between two different semantic units is estimated by their relative distance in the semantic space.…”

Section: Related Workmentioning

confidence: 99%

Automatic Scoring of English Essays Based on Machine Learning Technology in a Wireless Network Environment

Zhang

Shen

2022

Security and Communication Networks

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The English composition is an important indicator of English learners’ overall language skills and is asked in large-scale English examinations, both in China’s college entrance examinations and graduate examinations and in the TOEFL, GRE, and IELTS examinations in Europe and the United States. Some automatic scoring systems for English writing have been created in the United States and internationally, however the systems still have issues with generalization, accuracy, and error correction. In this paper, we present a method to improve the accuracy of existing automatic composition scoring systems through deep learning techniques in a wireless network environment. Experiments reveal that the method can accurately assess the quality of English learners’ writings, paving the way for the creation of an automated composition scoring system for large-scale machine testing and web-based self-learning platforms.

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Section: Related Workmentioning

confidence: 99%