An Ethnography of Datasets: Difference between revisions
From Algolit
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by Algolit | by Algolit | ||
| − | We often start the monthly Algolit meetings by searching for datasets or trying to create them. | + | We often start the monthly Algolit meetings by searching for datasets or trying to create them. Sometimes we use already-existing corpora, made available through the Natural Language Toolkit [http://www.nltk.org/ nltk]. NLTK contains, among others, The Universal Declaration of Human Rights, inaugural speeches from US presidents, or movie reviews from the popular site Internet Movie Database (IMDb). Each style of writing will conjure different relations between the words and will reflect the moment in time from which they originate. In this sense, the Python package manager for natural language processing could be regarded as a time capsule. The material that was selected to be included was deemed useful for at least one community, yet it is perceived as a universal default through the ease with which it is made available. |
| − | With this work, we look at the datasets most commonly used by data scientists to train machine algorithms. What material do they consist of? Who collected them? When? | + | With this work, we look at the datasets most commonly used by data scientists to train machine algorithms. What material do they consist of? Who collected them? When? |
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Revision as of 21:06, 21 March 2019
by Algolit
We often start the monthly Algolit meetings by searching for datasets or trying to create them. Sometimes we use already-existing corpora, made available through the Natural Language Toolkit nltk. NLTK contains, among others, The Universal Declaration of Human Rights, inaugural speeches from US presidents, or movie reviews from the popular site Internet Movie Database (IMDb). Each style of writing will conjure different relations between the words and will reflect the moment in time from which they originate. In this sense, the Python package manager for natural language processing could be regarded as a time capsule. The material that was selected to be included was deemed useful for at least one community, yet it is perceived as a universal default through the ease with which it is made available.
With this work, we look at the datasets most commonly used by data scientists to train machine algorithms. What material do they consist of? Who collected them? When?
Concept & interface: Cristina Cochior