In prominent theories pronouns are treated as having (in effect) descriptive content, to explain the possibility of anaphora on indefinites outside their binding domain. We discuss some data that appears to be problematic for these approaches, and consider as an alternative, a view that treats pronouns as simple variables and tracks anaphoric dependencies separately.

Using Transparent Machine Learning to study Human Speech   Machine learning, the use of nuanced computer models to analyze and predict data, has a long history in speech recognition and natural language processing, but has largely been limited to more applied, engineering tasks.  This talk will describe two more research-focused applications of transparent machine learning algorithms in the study of speech perception and production. For speech perception, we’ll examine the difficult problem of identifying acoustic cues to a complex phonetic…

Topic situations and domain restriction      Lyn Frazier University of Massachusetts Amherst Topic Situations are discussed in several guises in the linguistics literature. Austinian Topic Situations (Austin, 1950) are familiar in situation semantics, where sentences are true of partial worlds, not entire worlds, and people hold attitudes toward partial worlds (Barwise and Perry, 1983, Kratzer, 1989, 2017). McKenzie (2015) discusses non-canonical switch reference in Kiowa (citing Watkins, 1993) using the example in (1), where the ‘same subject’ marker gàu is used to indicate that…