README.md

---
language:
  - en
multilinguality:
  - monolingual
license: other
license_name: topicnet
license_link: >-
  https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/LICENSE.txt
configs:
  - config_name: "bag-of-words"
    default: true
    data_files:
      - split: train
        path: "data/Reuters_BOW.csv.gz"
  - config_name: "natural-order-of-words"
    data_files:
      - split: train
        path: "data/Reuters_NOOW.csv.gz"
task_categories:
  - text-classification
task_ids:
  - topic-classification
  - multi-class-classification
  - multi-label-classification
tags:
  - topic-modeling
  - topic-modelling
  - text-clustering
  - multimodal-data
  - multimodal-learning
  - modalities
  - document-representation
---


# Reuters

The Reuters Corpus contains 10,788 news documents totaling 1.3 million words. The documents have been classified into 90 topics, and grouped into two sets, called "training" and "test"; thus, the text with fileid 'test/14826' is a document drawn from the test set. This split is for training and testing algorithms that automatically detect the topic of a document, as we will see in chap-data-intensive.

* Language:           English
* Number of topics:   90
* Number of articles: ~10.000
* Year:               2000


## References

* NLTK datasets: https://www.nltk.org/book/ch02.html.
* Dataset site: https://trec.nist.gov/data/reuters/reuters.html.