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3 results for “conceptnet”
ConceptNet Vector Ensemble 16.04 input data
<p>This is the data required to build the paper "An Ensemble Method to Build High-Quality Word Embeddings", by Robyn Speer and Joshua Chin.</p> <p>The input data itself comes from:</p> <ul> <li> <p><a href="http://conceptnet5.media.mit.edu/">ConceptNet 5.4</a>, which contains data from Wiktionary, WordNet, and many contributors to Open Mind Common Sense projects, edited by Robyn Speer</p> </li> <li> <p><a href="http://nlp.stanford.edu/projects/glove/">GloVe</a>, by Jeffrey Pennington, Richard Socher, and Christopher Manning</p> </li> <li> <p><a href="https://code.google.com/archive/p/word2vec/">word2vec</a>, by Tomas Mikolov and Google Research</p> </li> <li> <p><a href="http://www.cis.upenn.edu/~ccb/ppdb/">PPDB</a>, by Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch</p> </li> </ul>
Input Data for "Distinguishing attributes using ConceptNet Numberbatch"
<p>In a post on blog.conceptnet.io, we're showing how to use ConceptNet Numberbatch alone to create a good solution to SemEval-2018 Task 10, Capturing Discriminative Attributes. This is an alternative, simplified implementation of a result presented in the SemEval paper <a href="http://aclweb.org/anthology/S18-1162">Distinguishing Attributes Using Text Corpora and Relational Knowledge</a>, by Robyn Speer and Joanna Lowry-Duda.</p> <p>This data repository contains the data necessary to make the simplified implementation work.</p>
ConceptNet 5.x Raw Data
<p>This archive contains the raw data that ConceptNet 5 is built from. More information about ConceptNet is available at http://conceptnet.io.</p> <p>If you use ConceptNet as part of another work, you must attribute ConceptNet and you must not restrict its license terms. For more license information: https://creativecommons.org/licenses/by-sa/4.0/</p> <p>ConceptNet has been developed by:</p> <p>* The MIT Media Lab, through various groups at different times:</p> <p> - Commonsense Computing<br> - Software Agents<br> - Digital Intuition</p> <p>* The Commonsense Computing Initiative, a worldwide collaboration with<br> contributions from:</p> <p> - National Taiwan University<br> - Universidade Federal de São Carlos<br> - Hokkaido University<br> - Tilburg University<br> - Nihon Unisys Labs<br> - Dentsu Inc.<br> - Kyoto University<br> - Yahoo Research Japan</p> <p>* Luminoso Technologies, Inc.</p> <p>Significant amounts of data were imported from:</p> <p>* 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</p> <p>ConceptNet also takes input from these sources of distributional word embeddings:</p> <p>ConceptNet takes input from these sources of pre-computed distributional word embeddings:</p> <p>- GloVe: Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.<br> https://nlp.stanford.edu/projects/glove/</p> <p>- 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</p> <p>- fastText: Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching Word Vectors with Subword Information. http://fasttext.cc<br> </p> <p>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:</p> <p>* Push Singh<br> * Catherine Havasi<br> * Hugo Liu<br> * Hyemin Chung<br> * Robyn Speer<br> * Ken Arnold<br> * Yen-Ling Kuo<br> * Naoki Otani<br> </p>
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