Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact

Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Reuters Transcribed Subset Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset is created by reading out 200 files from the 10 largest Reuters classes and using an Automatic Speech Recognition system to create corresponding transcriptions.

Data Set Characteristics:  


Number of Instances:




Attribute Characteristics:


Number of Attributes:


Date Donated


Associated Tasks:


Missing Values?


Number of Web Hits:



Shourya Roy
shourya.roy '@'
Shantanu Godbole
shantanu '@'

Data Set Information:

Data Characteristics:
This data was created by selecting 20 files each from the 10 largest classes
in the Reuters-21578 collection
([Web Link]).
The files were read out by 3 Indian speakers and an Automatic Speech
Recognition (ASR) system was used to generate the transcripts. More about the
ASR system can be found in [1]. Such a dataset will be really helpful to
study the effect of speech recognition noise on text mining algorithms.
The first work which refered to this dataset was on noisy text classification[2].

Data Format:
There are 10 directories labeled by the topic name.
Each contains 20 files of transcriptions.

[1] L. R. Bahl, S. Balakrishnan-Aiyer, J. Bellegarda, M. Franz,
P. Gopalakrishnan, D. Nahamoo, M. Novak, M. Padmanabhan,
M. Picheny, and S. Roukos,
Performance of the IBM large vocabulary continuous speech recognition system on
the ARPA wall street journal task.
In Proc. of ICASSP ’95,
pages 41–44, Detroit, MI, 1995.
[2] S. Agarwal, S. Godbole, D. Punjani and S. Roy,
How Much Noise is too Much: A Study in Automatic Text Classification',
In Proc. of ICDM 2007

Attribute Information:

Provide information about each attribute in your data set.

Relevant Papers:

'“How Much Noise in Text is too Much: A Study in Automatic Document Classification”, ICDM 2007, Sumeet Agarwal, Shantanu Godbole, Diwakar Punjani and Shourya Roy

Citation Request:

Please refer to the Machine Learning Repository's citation policy

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML