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Modern medical diagnosis relies on precise pain assessment tools intranslating clinical information from patient to physician. The McGill PainQuestionnaire (MPQ) is a clinical pain assessment technique that utilizes 78adjectives of different intensities in 20 different categories to quantity apatient's pain. The questionnaire's efficacy depends on a predictable patternof adjective use by patients experiencing pain. In this study, I recreate theMPQ's adjective intensity orderings using data gathered from patient forums andmodern NLP techniques. I extract adjective intensity relationships by searchingfor key linguistic contexts, and then combine the relationship information toform robust adjective scales. Of 17 adjective relationships predicted by thisresearch, only 4 diverge from the MPQ's orderings, which is statisticallysignificant at the 0.1 alpha level. The results suggest predictable patterns ofadjective use by people experiencing pain, but call into question the MPQ'scategories for grouping adjectives.

Tipping the Scales: A Corpus-Based Reconstruction of Adjective Scales in the McGill Pain Questionnaire