The Annotator: Difference between revisions
From Algolit
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by Algolit | by Algolit | ||
| − | + | [https://gitlab.constantvzw.org/algolit/mundaneum/tree/master/exhibition/4-Informants/the-annotator Sources on Gitlab] | |
| − | The | + | The annotator asks for the guidance of visitors in annotating the archive of Mundaneum. |
| − | The labelling of a dataset is work executed by humans, they pick a label for each row of the dataset. To ensure the quality of the labels multiple annotators see the same row and have to give the same label before an example is included in the training data. Only | + | The annotation process is a crucial step in supervised machine learning where the algorithm is given examples of what it needs to learn. A spam filter in training will be fed examples of spam and real messages. These examples are entries, or rows from the dataset with a label, spam or non-spam. |
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| + | The labelling of a dataset is work executed by humans, they pick a label for each row of the dataset. To ensure the quality of the labels multiple annotators see the same row and have to give the same label before an example is included in the training data. Only when enough samples of each label have been gathered in the dataset can the computer start the learning process. | ||
In this interface we ask you to help us classify the cleaned texts from the Mundaneum archive to expand our training set and improve the quality of the installation 'Classifying the World' in Oracles. | In this interface we ask you to help us classify the cleaned texts from the Mundaneum archive to expand our training set and improve the quality of the installation 'Classifying the World' in Oracles. | ||
Latest revision as of 17:53, 4 June 2019
by Algolit
The annotator asks for the guidance of visitors in annotating the archive of Mundaneum.
The annotation process is a crucial step in supervised machine learning where the algorithm is given examples of what it needs to learn. A spam filter in training will be fed examples of spam and real messages. These examples are entries, or rows from the dataset with a label, spam or non-spam.
The labelling of a dataset is work executed by humans, they pick a label for each row of the dataset. To ensure the quality of the labels multiple annotators see the same row and have to give the same label before an example is included in the training data. Only when enough samples of each label have been gathered in the dataset can the computer start the learning process.
In this interface we ask you to help us classify the cleaned texts from the Mundaneum archive to expand our training set and improve the quality of the installation 'Classifying the World' in Oracles.
Concept, code, interface: Gijs de Heij