Viggo Kann | KTH Royal Institute of Technology (original) (raw)
Jag är en svensk professor i datalogi.
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Papers by Viggo Kann
Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE '14, 2014
Evaluation of word space models is usually local in the sense that it only considers words that a... more Evaluation of word space models is usually local in the sense that it only considers words that are deemed very similar by the model. We propose a global evaluation scheme based on clustering of the words. A clustering of high quality in an external evaluation against a semantic resource, such as a dictionary of synonyms, indicates a word space model of high quality. We use Random Indexing to create several different models and compare them by clustering evaluation against the People's Dictionary of Synonyms, a list of Swedish synonyms that are graded by the public. Most notably we get better results for models based on syntagmatic information (words that appear together) than for models based on paradigmatic information (words that appear in similar contexts). This is quite contrary to previous results that have been presented for local evaluation. Clusterings to ten clusters result in a recall of 83% for a syntagmatic model, compared to 34% for a comparable paradigmatic model, and 10% for a random partition.
Israel Symposium on Theory of Computing Systems, 1996
Proceedings of the 18th ACM conference on Innovation and technology in computer science education - ITiCSE '13, 2013
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE '14, 2014
Evaluation of word space models is usually local in the sense that it only considers words that a... more Evaluation of word space models is usually local in the sense that it only considers words that are deemed very similar by the model. We propose a global evaluation scheme based on clustering of the words. A clustering of high quality in an external evaluation against a semantic resource, such as a dictionary of synonyms, indicates a word space model of high quality. We use Random Indexing to create several different models and compare them by clustering evaluation against the People's Dictionary of Synonyms, a list of Swedish synonyms that are graded by the public. Most notably we get better results for models based on syntagmatic information (words that appear together) than for models based on paradigmatic information (words that appear in similar contexts). This is quite contrary to previous results that have been presented for local evaluation. Clusterings to ten clusters result in a recall of 83% for a syntagmatic model, compared to 34% for a comparable paradigmatic model, and 10% for a random partition.
Israel Symposium on Theory of Computing Systems, 1996
Proceedings of the 18th ACM conference on Innovation and technology in computer science education - ITiCSE '13, 2013
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
Complexity and Approximation, 1999
25 oktober 2017, prodekanus seminarieserie, KTH