Whamit!

Speaker: Isabella Senturia (Yale)
Title: On the Spectra of Syntactic Structures
Time: Tuesday, April 23rd, 1pm - 2pm
Location: 32-D461

Abstract: This talk explores the application of spectral graph theory to the problem of characterizing linguistically significant classes of tree structures. As a case study, we focus on three classes of trees, binary, X-bar, and asymmetric c-command extensional, as well as different types of syntactic movement, and show that the spectral properties of different matrix representations of these classes of trees provide insight into the properties that characterize these classes. More generally, our goal is to provide another route to understanding the structure of natural language, one that does not come from extensive definitions and rules taken by extrapolating from the syntactic structure, but instead is extracted directly from computation on the syntactically-defined graphical structures. We also discuss implications of this work for generative capacity and for the Minimalist program.

Speaker: Logan Swanson (Stony Brook)
Title: Phonotactic Learning with Abductive Principles
Time: Monday, April 22nd, 5pm - 6:30pm
Location: 32-D831

Abstract: A foundational learning problem in phonology is phonotactics: discovering the constraints which govern what configurations of sounds are licit in a particular language. These constraints are generally formulated over some kind of substructure, which could be segmental, feature-based, or autosegmental. These substructures form a partial order, with simpler structures contained within more complex ones.

In this talk I will present the Bottom-Up Factor Inference Algorithm (BUFIA), an algorithm which leverages this underlying structure to learn surface-true phonotactic constraints from positive data (Chandlee et al., 2019). In this sense, BUFIA offers a general form for phonotactic learning, since it can operate over any choice of representation and use any abductive principle to decide when to add constraints. While Rawski (2021) examined several possible constraint selection principles, my work has shown additional kinds of abductive pressures that can guide the search, including execution of the search path itself in addition to other constraint selection criteria.

I will discuss how the BUFIA framework provides insight into the impact of these factors on learning, and give a demo of the software implementation.

Speaker: Yiyang Guo (University of Cambridge/Harvard University)
Title: Event counting, eventuality, and aspect
Time: Thursday, April 25th, 12:30pm - 2pm
Location: 32-D461

Abstract: Counting in the domain of events can exhibit a two-level nesting structure, i.e., the event level and the occasion level (Cusic 1981), as illustrated by the use of stacked time-adverbials in English (Andrews 1983; Ernst 1994; Cinque 1999) and two types of verbal classifiers in Mandarin Chinese (Deng 2013; Donazzan 2013; Zhang 2017). In Mandarin Chinese, event counting interacts with eventuality types and aspect. In this talk, I will provide two main observations:

(i) Countability of events hinges on eventuality types. Specifically, statives cannot be counted, activities allow counting at both the occasion level and the event level, while achievements and accomplishments can only be counted at the occasion level.

(ii) Counting expressions display difference in their compatibility with aspect. They are incompatible with the progressive marker -zhe, compatible with the perfective marker -le, but non-uniformly compatible with the experiential perfect marker -guo.

To account for the distribution of counting expressions, I will propose an atom-based analysis of event-counting (cf. Bach 1986; Krifka 1989; Landman 2006) under the framework of neo-Davidsonian event semantics (Parsons 1990; Carlson 1984; a.o.).