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 r...
We present in this paper experiments with several semantic similarity measures based on the unsuperv...
AbstractIntroductionThis article explores how measures of semantic similarity and relatedness are im...
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...
In three studies we investigated whether LSA cosine values estimate human similarity ratings of word...
Pairwise similarity judgement correlations between humans and Latent Semantic Analysis (LSA) were ex...
The aims of this chapter include describing: how the semantic representations may be used to measure...
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...
[[abstract]]The purpose of this study is to apply latent semantic analysis (LSA) to analyze item sim...
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...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
We present in this paper experiments with several semantic similarity measures based on the unsuperv...
AbstractIntroductionThis article explores how measures of semantic similarity and relatedness are im...
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...
In three studies we investigated whether LSA cosine values estimate human similarity ratings of word...
Pairwise similarity judgement correlations between humans and Latent Semantic Analysis (LSA) were ex...
The aims of this chapter include describing: how the semantic representations may be used to measure...
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...
[[abstract]]The purpose of this study is to apply latent semantic analysis (LSA) to analyze item sim...
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...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and com...
We present in this paper experiments with several semantic similarity measures based on the unsuperv...
AbstractIntroductionThis article explores how measures of semantic similarity and relatedness are im...
Research into word meaning and similarity structure typically focus on highly related entities like ...