Computer assisted essay assessment
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Computer-assisted essay assessment. Similarity scores by Latent Semantic Analysis Comparison material based on relevant passages from textbook Defining threshold values for grade categories Grading the essays. Results. Latent Semantic Analysis (LSA) aka Latent Semantic Indexing (LSI).

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Computer assisted essay assessment
Computer-assisted essay assessment

  • Similarity scores by Latent Semantic Analysis

  • Comparison material based on relevant passages from textbook

  • Defining threshold values for grade categories

  • Grading the essays



Latent semantic analysis lsa aka latent semantic indexing lsi
Latent Semantic Analysis (LSA)aka Latent Semantic Indexing (LSI)

  • Several Applications

    • Information Retrieval

    • Information Filtering

    • Essay Assessment

  • Documents are presented as a matrix in which each row stands for a unique word and each column stands for a text passage (word-by-document matrix)

  • Truncated singular value decomposition is used to model latent semantic structure

  • Resulting semantic space is used for retrieval

  • Can retrieve documents that share no words with query .


Latent semantic analysis lsa
Latent Semantic Analysis (LSA)

  • Singular Value Decomposition

    • Reduces the dimensionality of word-by-document matrix

    • Using a reduced dimension new relationships between words and contexts are induced when reconstructing a close approximation to the original matrix

    • Reduces irrelevant data and “noise”


Latent semantic analysis lsa document comparison

Word-by-document matrix

Latent Semantic Analysis (LSA)Document comparison

  • Semantic space is constructed from the training material

  • To grade an essay, a matrix for the essay document is built

  • Document vector of essay is compared to the semantic space


Latent semantic analysis lsa1

A

B

Latent Semantic Analysis (LSA)

  • Document comparison

    • Euclidean distance

    • Dot product

    • Cosine measure

  • Cosine between document vectors

  • Dot product of vector divided by their lengths


Latent semantic analysis lsa2
Latent Semantic Analysis (LSA)

  • Pros

    • Doesn’t just match on terms, tries to match on concepts

  • Cons

    • Computationally expensive, its not cheap to compute singular values

    • Choice of dimensionalityis somewhat arbitrary, done by experimentation


Latent semantic analysis lsa3
Latent Semantic Analysis (LSA)

  • Word-by-document matrix


Latent semantic analysis lsa4
Latent Semantic Analysis (LSA)

  • Singular value decomposition


Latent semantic analysis lsa5
Latent Semantic Analysis (LSA)

  • Two dimensional reconstruction of word-by-document matrix



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