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A mathematical model called item response theory is often applied to high-stakes tests to estimate test-taker ability level and to determine the characteristics of test questions (i.e., items). Often, these tests contain subsets of items (testlets) grouped around a common stimulus. This grouping often leads to items within one testlet being more strongly correlated among themselves than among items from other testlets, which can result in moderate to strong testlet effects.

Text similarity measurement provides a rich source of information and is increasingly being used in the development of new educational and psychological applications. However, due to the high-stakes nature of educational and psychological testing, it is imperative that a text similarity measure be stable (or robust) to avoid uncertainty in the data. The present research was sparked by this requirement. First, multiple sources of uncertainty that may affect the computation of semantic similarity between two texts are enumerated.