Evaluating WordNet-based Measures of Lexical Semantic Relatedness

@article{Budanitsky2006EvaluatingWM,
  title={Evaluating WordNet-based Measures of Lexical Semantic Relatedness},
  author={Alexander Budanitsky and Graeme Hirst},
  journal={Computational Linguistics},
  year={2006},
  volume={32},
  pages={13-47}
}
The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why… 
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References

SHOWING 1-10 OF 73 REFERENCES
Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures
Five different proposed measures of similarity or semantic distance in WordNet were experimentally compared by examining their performance in a real-word spelling correction system. It was found that
Using Measures of Semantic Relatedness for Word Sense Disambiguation
TLDR
This paper generalizes the Adapted Lesk Algorithm to a method of word sense disambiguation based on semantic relatedness and finds that the gloss overlaps of AdaptedLesk and the semantic distance measure of Jiang and Conrath (1997) result in the highest accuracy.
WordNet::Similarity - Measuring the Relatedness of Concepts
WordNet::Similarity is a freely available software package that makes it possible to measure the semantic similarity and relatedness between a pair of concepts (or synsets). It provides six measures
Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy
This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the
Extended Gloss Overlaps as a Measure of Semantic Relatedness
TLDR
A new measure of semantic relatedness between concepts that is based on the number of shared words (overlaps) in their definitions (glosses) and reasonably correlates to human judgments is presented.
Automatic Retrieval and Clustering of Similar Words
TLDR
A word similarity measure based on the distributional pattern of words allows the automatically constructed thesaurus to be significantly closer to WordNet than Roget Thesaurus is.
Lexical Semantic Relatedness and Its Application in Natural Language Processing
TLDR
This report is a comprehensive study of recent computational methods of measuring lexical semantic relatedness, and a survey of methods, as well as their applications, is presented.
Non-Classical Lexical Semantic Relations
  • Jane Morris, Graeme Hirst
  • Linguistics
    Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics - CLS '04
  • 2004
NLP methods and applications need to take account not only of "classical" lexical relations, as found in WordNet, but the less-structural, more context-dependent "non-classical" relations that
Learning Semantic Classes for Word Sense Disambiguation
TLDR
It is shown that these general concepts from a sense tagged corpus can be transformed to fine grained word senses using simple heuristics, and applying the technique for recent SENSEVAL data sets shows that this approach can yield state of the art performance.
Text retrieval using inference in semantic metanetworks
TLDR
This dissertation presents techniques to automatically assign weights to network edges and determine semantic distance between arbitrary nodes, which allows word sense disambiguation during document and query indexing by minimizing mutual distance among word senses within a window of words with one or more senses each.
...
...