The power of normalised word vectors for automatically grading essays
Latent Semantic Analysis, when used for automated essay grading, makes use of document word count vectors for scoring the essays against domain knowledge. Words in the domain knowledge documents and essays are counted, and Singular Value Decomposition is undertaken to reduce the dimensions of the se...
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| Format: | Journal Article |
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The Informing Science Institute
2006
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| Online Access: | http://proceedings.informingscience.org/InSITE2006/IISITWill155.pdf http://hdl.handle.net/20.500.11937/46415 |
| _version_ | 1848757550599634944 |
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| author | Williams, Robert |
| author_facet | Williams, Robert |
| author_sort | Williams, Robert |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Latent Semantic Analysis, when used for automated essay grading, makes use of document word count vectors for scoring the essays against domain knowledge. Words in the domain knowledge documents and essays are counted, and Singular Value Decomposition is undertaken to reduce the dimensions of the semantic space. Near neighbour vector cosines and other variables are used to calculate an essay score. This paper discusses a technique for computing word count vectors where the words are first normalised using thesaurus concept index numbers. This approach leads to a vector space of 812 dimensions, does not require Singular Value Decomposition, and leads to a reduced computational load. The cosine between the vectors for the student essay and a model answer proves to be a very powerful independent variable when used in regression analysis to score essays. An example of its use in practice is discussed. |
| first_indexed | 2025-11-14T09:29:53Z |
| format | Journal Article |
| id | curtin-20.500.11937-46415 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:29:53Z |
| publishDate | 2006 |
| publisher | The Informing Science Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-464152017-01-30T15:27:08Z The power of normalised word vectors for automatically grading essays Williams, Robert Normalised Word Vectors Multiple Regression Analysis Singular Value ecomposition Automated Essay Grading Latent Semantic Analysis AEG Electronic Thesaurus Latent Semantic Analysis, when used for automated essay grading, makes use of document word count vectors for scoring the essays against domain knowledge. Words in the domain knowledge documents and essays are counted, and Singular Value Decomposition is undertaken to reduce the dimensions of the semantic space. Near neighbour vector cosines and other variables are used to calculate an essay score. This paper discusses a technique for computing word count vectors where the words are first normalised using thesaurus concept index numbers. This approach leads to a vector space of 812 dimensions, does not require Singular Value Decomposition, and leads to a reduced computational load. The cosine between the vectors for the student essay and a model answer proves to be a very powerful independent variable when used in regression analysis to score essays. An example of its use in practice is discussed. 2006 Journal Article http://hdl.handle.net/20.500.11937/46415 http://proceedings.informingscience.org/InSITE2006/IISITWill155.pdf The Informing Science Institute restricted |
| spellingShingle | Normalised Word Vectors Multiple Regression Analysis Singular Value ecomposition Automated Essay Grading Latent Semantic Analysis AEG Electronic Thesaurus Williams, Robert The power of normalised word vectors for automatically grading essays |
| title | The power of normalised word vectors for automatically grading essays |
| title_full | The power of normalised word vectors for automatically grading essays |
| title_fullStr | The power of normalised word vectors for automatically grading essays |
| title_full_unstemmed | The power of normalised word vectors for automatically grading essays |
| title_short | The power of normalised word vectors for automatically grading essays |
| title_sort | power of normalised word vectors for automatically grading essays |
| topic | Normalised Word Vectors Multiple Regression Analysis Singular Value ecomposition Automated Essay Grading Latent Semantic Analysis AEG Electronic Thesaurus |
| url | http://proceedings.informingscience.org/InSITE2006/IISITWill155.pdf http://hdl.handle.net/20.500.11937/46415 |