We present an application of Latent Semantic Analysis to word sense discrimination within a tutor for English vocabulary learning. We attempt to match the meaning of a word in a document with the meaning of the same word in a fill-in-the-blank question. We compare the performance of the Lesk algorithm to Latent Semantic Analysis. We also compare the performance of Latent Semantic Analysis on a ...

We present in this paper the results of our investigation on semantic similarity measures at wordand sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach. The focus is on similarity measures based on Latent Dirichlet Allocation, due to its novelty ...

We study and propose in this article several novel solutions to the task of semantic similarity between two short texts. The proposed solutions are based on the probabilistic method of Latent Dirichlet Allocation (LDA) and on the algebraic method of Latent Semantic Analysis (LSA). Both methods, LDA and LSA, are completely automated methods used to discover latent topics or concepts from large c...

We present in this demo SEMILAR, a SEMantic similarity toolkit. SEMILAR includes offers in one software environment several broad categories of semantic similarity methods: vectorial methods including Latent Semantic Analysis, probabilistic methods such as Latent Dirichlet Allocation, greedy lexical matching methods, optimal lexico-syntactic matching methods based on word-to-word similarities a...

Abstract The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrix have been shown to mainly display marginal effects, which are irrelevant for information retrieval. To improve the performance LSA, usually elements raw weighted and weighting exponent singular values can be adjusted. An alternative retrieval technique that ignores effects is correspondence (CA...

This paper provides system description of the cross-level semantic similarity task for the SEMEVAL-2014 workshop. Crosslevel semantic similarity measures the degree of relatedness between texts of varying lengths such as Paragraph to Sentence and Sentence to Phrase. Latent Semantic Analysis was used to evaluate the cross-level semantic relatedness between the texts to achieve above baseline sco...