Frequent Term Distribution Measures for Dataset Profiling

Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC 2004)

Abstract

We motivate the need for dataset profiling in the context of evaluation, and show that textual datasets differ in ways that challenge assumptions about the applicability of techniques. We set out some criteria for useful profiling measures. We argue that distribution patterns of frequent words are useful in profiling genre, and report on a series of experiments with ?2 based measures on the TIPSTER collection, and on textual intranet data. Findings show substantial differences in the distribution of very frequent terms across datasets.