ConceptNet 5.6 Raw Data Article Swipe
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· 2018
· Open Access
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· DOI: https://doi.org/10.5281/zenodo.1165009
· OA: W4393810043
This archive contains the raw data that ConceptNet 5 is built from. More information about ConceptNet is available at http://conceptnet.io. ConceptNet has been developed by: * The MIT Media Lab, through various groups at different times: - Commonsense Computing<br> - Software Agents<br> - Digital Intuition * The Commonsense Computing Initiative, a worldwide collaboration with<br> contributions from: - National Taiwan University<br> - Universidade Federal de São Carlos<br> - Hokkaido University<br> - Tilburg University<br> - Nihon Unisys Labs<br> - Dentsu Inc. * Luminoso Technologies, Inc. Significant amounts of data were imported from: * WordNet, a project of Princeton University<br> * Wikipedia and Wiktionary, collaborative projects of the Wikimedia Foundation<br> * Luis von Ahn's "Games with a Purpose"<br> * DBPedia<br> * OpenCyc<br> * JMDict, by Jim Breen ConceptNet also takes input from these sources of distributional word embeddings: ConceptNet takes input from these sources of pre-computed distributional word embeddings: - GloVe: Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.<br> https://nlp.stanford.edu/projects/glove/ - word2vec: Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In Computing Research Repository. http://dblp.org/rec/bib/journals/corr/abs-1301-3781 - fastText: Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching Word Vectors with Subword Information. http://fasttext.cc<br> Here is a short, incomplete list of people who have made significant<br> contributions to the development of ConceptNet as a data resource, roughly in<br> order of appearance: * Push Singh<br> * Catherine Havasi<br> * Hugo Liu<br> * Hyemin Chung<br> * Robyn Speer<br> * Ken Arnold<br> * Yen-Ling Kuo
