The Reuters Corpus Volume 1 -from Yesterday’s News to Tomorrow’s Language Resources

By Tony G. Rose, Mark Stevenson, Miles Whitehead. International Conference on Language Resources and Evaluation, 2002

351 citations recorded by the source corpus.

Reuters, the global information, news and technology group, has for the first time made available free of charge, large quantities of archived Reuters news stories for use by research communities around the world. The Reuters Corpus Volume 1 (RCV1) includes over 800,000 news stories typical of the annual English language news output of Reuters. This paper describes the origins of RCV1, the motivations behind its creation, and how it differs from previous corpora. In addition we discuss the system of category coding, whereby each story is annotated for topic, region and industry sector. We also discuss the process by which these codes were applied, and examine the issues involved in maintaining quality and consistency of coding in an operational, commercial environment.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/590cfa38094122f42412d747350229f79b7e6412

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

An analysis of the relative hardness of Reuters-21578 subsets (2005, background): “1While a new Reuters corpus has recently been made available for TC research [20, 27], its takeup has been somehow slow, and also hindered by terms of use that are not universally acceptable by interested parties.”
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems (2013, methodology): “After experimenting with several corpus choices including Wikipedia, Project Gutenberg e-Books (Hart, 1997), ukWaC (Baroni et al., 2009), Reuters News stories (Rose et al., 2002) and LDC gigawords, we selected the Web corpus from the Stanford WebBase project (Stanford, 2001).”
InterActive Feature Selection (2005, methodology): “Since this is not the case for all the classification problems we have chosen, we modify the definition of deficiency, and define it in terms of the F 1 measure (harmonic mean of precision and recall [Rose et al., 2002]).”
Abstractive Meeting Summarization Using Dependency Graph Fusion (2015, neutral): “In this work, we calculate these values using Reuters corpora [5] to obtain dominant relations from non-conversational style of text.”
A Probabilistic Classification Approach for Lexical Textual Entailment (2005, methodology): “Experiments were done on the Reuters Corpus Volume 1 (Rose et al. 2002) - a collection of about 810,000 English News stories most of which are economy related.”
A Probabilistic Setting and Lexical Coocurrence Model for Textual Entailment (2005, methodology): “An annotator was asked to choose 60 hypotheses based on sentences from the first few documents in the Reuters Corpus Volume 1 (Rose et al., 2002).”
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances (2011, background): “MNB models the distribution of features as a multinomial: documents are sequences of words, with the “naı̈ve” assumption that words in each position are generated independently.”
Finding Predominant Word Senses in Untagged Text (2004, background): “The Reuters corpus (Rose et al., 2002) is a collection of about 810,000 Reuters, English Language News stories.”
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation (2014, background): “Models of association, on the other hand, may be better suited to tasks such as word-sense disambiguation (Navigli, 2009), and applications such as text classiﬁcation (Phan et al., 2008) in which the target classes correspond to topical domains such as agriculture or sport (Rose et al., 2002).”