A Figure of Merit for the Evaluation of Web-Corpus Randomness

In this paper, we present an automated, quantitative, knowledge-poor method to evaluate the randomness of a collection of documents (corpus), with respect to a number of biased partitions. The method is based on the comparison of the word frequency distribution of the target corpus to word frequency distributions from corpora built in deliberately biased ways. We apply the method to the task of building a corpus via queries to Google. Our results indicate that this approach can be used, reliably, to discriminate biased and unbiased document collections and to choose the most appropriate query terms.

• Type: Conference
• Year: 2006
• Where: EACL
• Authors: Massimiliano Ciaramita, Marco Baroni
• Keywords: EACL 2006, Natural Language Processing, Target Corpus, Unbiased Document Collections, Word Frequency Distributions