Using Cross Language Analysis to Investigate Factors for Differential Marking

Indiana University, Bloomington IN

Investigators

Abstract

This project uses cross language analysis to investigate factors for 'differential marking.' In linguistics, 'differential marking' refers to morphological patterning where a nominal, such as a subject, occurs with special encoding. With this encoding, a speaker can communicate non-grammatical information about that nominal, such as surprise, unpredictability, or unexpectedness of the involvement of an entity in an event. Speakers do not consciously use differential marking to package information. Rather, there appear to be a complex combination of grammatical, discourse, semantic, and pragmatic factors that predict differential marking, including inherent properties of the noun (e.g., person, animacy, or count versus mass), properties of the predicate (transitivity, completed action), or the position of a nominal in longer connected speech (e.g., mentioned for the first time in a conversation or story). The project will include the training of students in coding and grammatical analysis. Language data, the coding protocol, and Python-based tools will be archived and freely accessible at University of North Texas Digital Library and/or through a GitHub repository. This project uses an innovative documentary method to gather information on factors determining differential marking. First, native speaking linguists of the investigated languages will code nominals in connected discourse for factors associated with differential marking. When non-speakers analyze data for differential marking, nuanced meanings can be lost in translation. Coding by trained native speakers will more accurately capture the meanings intended by the speaker. The project will develop a coding manual to standardize coding. Second, discussions about the data with groups of non-linguist speakers will be used to refine coding. Group discussions on grammar tend to evoke scenarios of usage and interpretation that are not recalled by investigators working on data on their own. Third, the project will develop Python-based tools to compare factors for differential marking across various data sets, both within one language and across different languages, to find statistically salient correspondences between factors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.