In three studies we investigated whether LSA cosine values estimate human similarity ratings of word pairs. In study 1 we found that LSA can distinguish between highly similar and dissimilar matches to a target word, but that it does not reliably distinguish between highly similar and less similar matches. In study 2 we showed that, using an expanded item set, the correlation between LSA ratings and human similarity ratings is both quite low and inconsistent. Study 3 demonstrates that, while people distinguish between taxonomic / thematic word pairs, LSA cosines do not. Although people rate taxonomically related items to be more similar than thematically related items, LSA cosine values are equivalent across stimuli types. Our results indic...
We study and propose in this article several novel solutions to the task of semantic similarity betw...
Latent Semantic Analysis (LSA) uses semantic correlations across a corpora to reduce problems with p...
Research into word meaning and similarity structure typically focus on highly related entities like ...
In three studies we investigated whether LSA cosine values estimate human similarity ratings of wor...
Pairwise similarity judgement correlations between humans and Latent Semantic Analysis (LSA) were ex...
[[abstract]]The purpose of this study is to apply latent semantic analysis (LSA) to analyze item sim...
The aims of this chapter include describing: how the semantic representations may be used to measure...
Data sets, variable descriptions and R scripts for the following article:<br> <p>Günther, F., Dudsch...
Many machine learning and data mining algorithms crucially rely on the similarity metrics. The Cosin...
We present in this paper experiments with several semantic similarity measures based on the unsuperv...
To study concepts that are coded in language, researchers often collect lists of conceptual properti...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
AbstractIntroductionThis article explores how measures of semantic similarity and relatedness are im...
International audienceIn Distributional Semantic Models (DSMs), Vector Cosine is widely used to esti...
<p>The keyword “stroke” is used as an example here. (a) The result for “stroke” by LSA method. (b) T...
We study and propose in this article several novel solutions to the task of semantic similarity betw...
Latent Semantic Analysis (LSA) uses semantic correlations across a corpora to reduce problems with p...
Research into word meaning and similarity structure typically focus on highly related entities like ...
In three studies we investigated whether LSA cosine values estimate human similarity ratings of wor...
Pairwise similarity judgement correlations between humans and Latent Semantic Analysis (LSA) were ex...
[[abstract]]The purpose of this study is to apply latent semantic analysis (LSA) to analyze item sim...
The aims of this chapter include describing: how the semantic representations may be used to measure...
Data sets, variable descriptions and R scripts for the following article:<br> <p>Günther, F., Dudsch...
Many machine learning and data mining algorithms crucially rely on the similarity metrics. The Cosin...
We present in this paper experiments with several semantic similarity measures based on the unsuperv...
To study concepts that are coded in language, researchers often collect lists of conceptual properti...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
AbstractIntroductionThis article explores how measures of semantic similarity and relatedness are im...
International audienceIn Distributional Semantic Models (DSMs), Vector Cosine is widely used to esti...
<p>The keyword “stroke” is used as an example here. (a) The result for “stroke” by LSA method. (b) T...
We study and propose in this article several novel solutions to the task of semantic similarity betw...
Latent Semantic Analysis (LSA) uses semantic correlations across a corpora to reduce problems with p...
Research into word meaning and similarity structure typically focus on highly related entities like ...