This article presents a novel bootstrapping approach for improving the quality of feature vector weighting in distributional word similarity. The method was motivated by attempts to utilize distributional similarity for identifying the concrete semantic relationship of lexical entailment. Our analysis revealed that a major reason for the rather loose semantic similarity obtained by distributional similarity methods is insufficient quality of the word feature vectors, caused by deficient feature weighting. This observation led to the definition of a bootstrapping scheme which yields improved feature weights, and hence higher quality feature vectors. The under-lying idea of our approach is that features which are common to similar words are a...
Most traditional distributional similarity models fail to capture syntagmatic patterns that group to...
Measuring the semantic similarity and relatedness of words is important for many natural language pr...
Most traditional distributional similarity models fail to capture syntagmatic patterns that group to...
This paper aims to re-think the role of the word similarity task in distributional semantics researc...
The dependency of word similarity in vector space models on the frequency of words has been noted in...
The dependency of word similarity in vector space models on the frequency of words has been noted in...
Part 10: Machine Learning - Natural LanguageInternational audienceMeasuring the semantic similarity ...
In this paper, we consider two applications of distributional similarity measures, probability estim...
In distributional semantics words are represented by aggregated context features. The similarity of ...
The dependency of word similarity in vector space models on the frequency of words has been noted in...
In distributional semantics, the unsupervised learning approach has been widely used for a large num...
A fundamental principle in distributional semantic models is to use similarity in linguistic environ...
A fundamental principle in distributional semantic models is to use similarity in linguistic environ...
Opportunities for associationist learning of word meaning, where a word is heard or read contemperan...
Opportunities for associationist learning of word meaning, where a word is heard or read contemperan...
Most traditional distributional similarity models fail to capture syntagmatic patterns that group to...
Measuring the semantic similarity and relatedness of words is important for many natural language pr...
Most traditional distributional similarity models fail to capture syntagmatic patterns that group to...
This paper aims to re-think the role of the word similarity task in distributional semantics researc...
The dependency of word similarity in vector space models on the frequency of words has been noted in...
The dependency of word similarity in vector space models on the frequency of words has been noted in...
Part 10: Machine Learning - Natural LanguageInternational audienceMeasuring the semantic similarity ...
In this paper, we consider two applications of distributional similarity measures, probability estim...
In distributional semantics words are represented by aggregated context features. The similarity of ...
The dependency of word similarity in vector space models on the frequency of words has been noted in...
In distributional semantics, the unsupervised learning approach has been widely used for a large num...
A fundamental principle in distributional semantic models is to use similarity in linguistic environ...
A fundamental principle in distributional semantic models is to use similarity in linguistic environ...
Opportunities for associationist learning of word meaning, where a word is heard or read contemperan...
Opportunities for associationist learning of word meaning, where a word is heard or read contemperan...
Most traditional distributional similarity models fail to capture syntagmatic patterns that group to...
Measuring the semantic similarity and relatedness of words is important for many natural language pr...
Most traditional distributional similarity models fail to capture syntagmatic patterns that group to...