Whamit!

The Weekly Newsletter of MIT Linguistics

LingLunch 4/16 - Yurika Aonuki (MIT) & William Pacheco (MIT)

Speaker: Yurika Aonuki (MIT) & William Pacheco (MIT)
Title: Questions and conditionals with disjunction in Gitksan & My Language, My Tools: AI-Assisted Documentation of Kiːwɑ Keres
Time: Thursday, April 16, 12:30pm – 2pm
Location: 32-D461

This week’s LingLunch will consist of two talks.

Talk 1: Questions and conditionals with disjunction in Gitksan (Yurika Aonuki, MIT)
Abstract: A morpheme ji in Gitksan (Rigsby 1986; Hunt 1993; Aonuki 2025) (and cognates in Nisga’a (Tarpent 1987) and Sm’algyax (Sasama 2001; Brown 2022, 2023, 2024, 2026)) introduce conditional antecedents and embedded polar questions. At the same time, ji is also allowed in declarative attitude complements when the attitude holder is uncertain or wrong (Hunt 1993; Gogag et al. in prep; Aonuki 2025); similar patterns are observed in Sm’algyax (Brown 2022, 2023). I will argue that there are reasons to attempt a unified analysis of ji in conditional antecedents and questions on one hand and declarative complements on the other. Against this background, I will provide novel data from disjunction, which shows that ji can introduce each disjunct in conditional antecedents, embedded alternative questions, and polar questions with disjunction. I will propose that ji constrains the set of epistemic possibilities associated with a speech or thought event, via the alternative semantic value of its argument. The proposal highlights a shared component between questions and uncertainty and has implications for an ongoing debate about the semantic relationship between polar and alternative questions (Roelofsen and Van Gool 2010; Pruitt and Roelofsen 2011; Meertens 2021).

*This will be a practice talk for GLOW and SULA-TripleA.

Talk 2: My Language, My Tools: AI-Assisted Documentation of Kiːwɑ Keres (William Pacheco, MIT)
Abstract: Kiːwɑ Wɛkɑ̤ɲi is an underdocumented dialect of Keres — a language isolate spoken in seven Pueblos in New Mexico. I am a native speaker and relatively new to linguistics, and I quickly found that the tools available for documenting my dialect weren’t built with my needs in mind.

Over the past few months I’ve been using AI (Claude, ChatGPT) to generate Python scripts and browser-based tools to organize, analyze, and visualize self-elicited data for my thesis work. A key condition from the start: data stays local, analysis happens offline, and I stay in control of the process.
 
The result is a suite of tools I call MorphemeStudio — a dictionary, phonology inventory, elicitation lab, and various utilities for extracting and documenting linguistic data efficiently. It’s also designed with community use in mind, so that the work can come back to the people whose language it is.
 
I’ll talk honestly about what this process actually looks like — how to work with AI as a collaborator, how to set parameters that keep you in control of your data and your analysis, and what kinds of tasks it’s actually good at. The broader takeaway: even if you don’t know how to code, there’s a workflow here that can work for you.
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