Reuters RCV1 RCV2 Multilingual, Multiview Text Categorization Test collection
Donated on 9/5/2013
This test collection contains feature characteristics of documents originally written in five different languages and their translations, over a common set of 6 categories.
Dataset Characteristics
Multivariate
Subject Area
Business
Associated Tasks
Classification
Feature Type
Real
# Instances
111740
# Features
-
Dataset Information
Additional Information
Uncompressing rcv1rcv2aminigoutte.tar.bz2 will create a directory that contains 5 subdirectories EN, FR, GR, IT and SP, corresponding to the 5 languages. Each subdirectory in {EN, FR, GR, IT, SP} contains 5 files, each containing indexes of the documents written or translated in that language. For example, EN contains files: - Index_EN-EN : Original English documents - Index_FR-EN : French documents translated to English - Index_GR-EN : German documents translated to English - Index_IT-EN : Italian documents translated to English - Index_SP-EN : Spanish documents translated to English And similarly for the 4 other languages. Each file contains one indexed document per line, in a format similar to SVM_light. Each line is of the form: <cat> <feature>:<value> <feature>:<value> ... where <cat> is the category label, ie one of C15, CCAT, E21, ECAT, GCAT or M11. <feature>:<value> is the feature, value pair, in ascending order of feature index. The order of documents is maintained in corresponding files, for example, FR/Index_EN-FR and EN/Index_EN-EN have the same number of documents (and therefore the same number of lines), in the same order.
Has Missing Values?
No
Variable Information
We focused on six relatively populous categories: C15, CCAT, E21, ECAT, GCAT, M11. For each language and each class, we sampled up to 5000 documents from the RCV1 (for English) or RCV2 (for other languages). Documents belonging to more than one of our 6 classes were assigned the label of their smallest class. This resulted in 12-30K documents per language, and 11-34K documents per class. The distribution of documents over languages and classes are: Number of Vocabulary Language documents percentage size ************ ********** ************ ************ English 18,758 16.78 21,531 French 26,648 23.45 24,893 German 29,953 26.80 34,279 Italian 24,039 21.51 15,506 Spanish 12,342 11.46 11,547 ------- Total 111,740 The distribution of classes in the whole collection is Number of Class documents percentage ********* ********** ************ C15 18,816 16.84 CCAT 21,426 19.17 E21 13,701 12.26 ECAT 19,198 17.18 GCAT 19,178 17.16 M11 19,421 17.39 In experiments that we conducted in cite{AUG09}, we considered each document available in a given language as the observed view for an example and all translated documents were used as the other views for that example, generated using Machine Translation. Results shown in this study were averaged over 10 random samples of 10 labeled examples per view for training, and 20% of the collection for testing.
Dataset Files
| File | Size |
|---|---|
| rcv1rcv2aminigoutte.tar.bz2 | 158.7 MB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset reuters_rcv1_rcv2_multilingual_multiview_text_categorization_test_collection = fetch_ucirepo(id=259) # data (as pandas dataframes) X = reuters_rcv1_rcv2_multilingual_multiview_text_categorization_test_collection.data.features y = reuters_rcv1_rcv2_multilingual_multiview_text_categorization_test_collection.data.targets # metadata print(reuters_rcv1_rcv2_multilingual_multiview_text_categorization_test_collection.metadata) # variable information print(reuters_rcv1_rcv2_multilingual_multiview_text_categorization_test_collection.variables)
Amini, M. & Goutte, C. (2009). Reuters RCV1 RCV2 Multilingual, Multiview Text Categorization Test collection [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5FS5R.
Creators
Massih-Reza Amini
Cyril Goutte
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.