This website is organized into three content areas: Information, Applications, and Demonstrations. We recommend you view that page after reading this summary. Latent Semantic Analysis LSA captures the essential relationships between text documents and word meaning, or semantics, the knowledge base which must be accessed to evaluate the quality of content. Several educational applications that employ LSA have been developed: 1 selecting the most appropriate text for learners with variable levels of background knowledge, 2 automatically scoring the content of an essay, and 3 helping students effectively summarize material. An LSA Primer.
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Latent semantic analysis LSA is a technique in natural language processing , in particular distributional semantics , of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text the distributional hypothesis. A matrix containing word counts per document rows represent unique words and columns represent each document is constructed from a large piece of text and a mathematical technique called singular value decomposition SVD is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by taking the cosine of the angle between the two vectors or the dot product between the normalizations of the two vectors formed by any two columns.
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The latent semantic analysis model is a theory of how the meaning of representations can be learned by finding large samples of language without explicit instructions on how it is structured. To extract and understand patterns from documents, Latent Semantic Analysis inherently follows certain assumptions:. The meaning of sentences or documents is a sum of the meaning of all the words that appear in it. In general, the meaning of a certain word is an average in all the documents in which it appears. Also, Latent Semantic Analysis assumes that semantic associations between words are not explicitly present, but only latently in the large sample of language.
Landauer , University of Colorado. The IEA uses Latent Semantic Analysis LSA , which is both a computational model of human knowledge representation and a method for extracting semantic similarity of words and passages from text. Simulations of psycholinguistic phenomena show that LSA reflects similarities of human meaning effectively. To assess essay quality, LSA is first trained on domain-representative text.