Trialling corpus search techniques for identifying person-first and identity-first language

Abstract

This short ‘methods’ article compares results for six different corpus search techniques for identifying person-first language (e.g. person/people with obesity, person/people with mental illness) and identity-first language (e.g. obese person/people, mentally ill person/people) in a corpus. This distinction is relevant across a range of health contexts, including but not limited to obesity, diabetes, or mental illness. Consequently, there is considerable interest in corpus linguistics and beyond in identifying the frequency of such language in large corpora. However, there is no consensus regarding the specific corpus search techniques to be used for this purpose. This article therefore offers a relevant methodological contribution, based on a trial of six different search techniques. Results from each technique are compared with respect to four different parameters: raw frequency, proportional usage, number of types identified (a proxy for ‘recall’) and false positives (a proxy for ‘precision’). This comparison in turn provides a basis for recommendations for future corpus linguistic studies of person- and identity-first language. The corpus that we use for this trial is a 16.4 million word corpus with newspaper articles containing the word obesity or obese. However, the findings should be relevant to other kinds of identity where similar syntactic structures are at play for expressing identity-first and person-first language.