Individual Differences in the Interpretation of Text: Implications for Information Science by Jane Morris demonstrates that different readers have different perceptions of lexical cohesion in a text. About 40% worth’s of difference. That is a difference in the meaning of the text.
Many tasks in library and information science (e.g., indexing, abstracting, classification, and text analysis techniques such as discourse and content analysis) require text meaning interpretation, and, therefore, any individual differences in interpretation are relevant and should be considered, especially for applications in which these tasks are done automatically. This article investigates individual differences in the interpretation of one aspect of text meaning that is commonly used in such automatic applications: lexical cohesion and lexical semantic relations. Experiments with 26 participants indicate an approximately 40% difference in interpretation. In total, 79, 83, and 89 lexical chains (groups of semantically related words) were analyzed in 3 texts, respectively. A major implication of this result is the possibility of modeling individual differences for individual users. Further research is suggested for different types of texts and readers than those used here, as well as similar research for different aspects of text meaning.
I won’t belabor what a 40% difference in interpretation implies for the one interpretation of data crowd. At least for those who prefer an evidence versus ideology approach to IR.
What is worth belaboring is how to use Morris’ technique to demonstrate such differences in interpretation to potential topic map customers. As a community we could develop texts for use with particular market segments, business, government, legal, finance, etc. An interface to replace the colored pencils used to mark all words belonging to a particular group. Automating some of the calculations and other operations on the resulting data.
Sensing that interpretations of texts vary is one thing. Having an actual demonstration, possibly using texts from a potential client, is quite another.
This is a tool we should build. I am willing to help. Who else is interested?