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Enhancing Essay Assessment with Latent Semantic Analysis

Explore how Latent Semantic Analysis (LSA) improves essay grading by analyzing similarity scores and defining threshold values based on relevant passages from textbooks. Learn how LSA constructs a semantic space for document comparison using techniques like Singular Value Decomposition. Discover the pros and cons of LSA in computer-assisted essay assessment.

dorothywebb
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Enhancing Essay Assessment with Latent Semantic Analysis

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  1. 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

  2. Results

  3. 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 .

  4. 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”

  5. 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

  6. 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

  7. 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

  8. Latent Semantic Analysis (LSA) • Word-by-document matrix

  9. Latent Semantic Analysis (LSA) • Singular value decomposition

  10. Latent Semantic Analysis (LSA) • Two dimensional reconstruction of word-by-document matrix

  11. Latent Semantic Analysis (LSA)

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