Materials, data, and analyses for experiments investigating the syntax-semantics of propositional attitude verbs
JavaScript HTML Python R
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.



This repository contains materials, data, and analysis scripts for one acceptability judgment experiment and two semantic similarity task investigating the relationship between propositional attitude verb syntax and semantics. These experiments were designed and run by Aaron Steven White, in consultation with Valentine Hacquard and Jeffrey Lidz using Alex Drummond's Ibex. The experiments were hosted on Ibex Farm and deployed on Amazon Mechanical Turk.

A small portion of these data and analyses was presented at NELS 43 at CUNY. A prepublication version of the proceedings paper for that conference, "Discovering classes of attitude verbs using subcategorization frame distributions," can be found in the papers/ directory. A journal-length paper that supersedes the NELS 43 paper, "Projecting Attitudes," is also now available in the papers/ directory.



This directory contains all the materials needed to run each of the three experiments. If you are interested in replicating or extending the acceptability judgment and generalized semantic discrimination judgment (triad) experiments specifically and would like to use these materials, please contact Aaron Steven White. These two experiments were run in 2012 and can be deployed in a more streamlined fashion, which the authors would be happy to assist other researchers with.


This directory contains directories for each experiment's data. Included in each directory are the raw data file pulled from Ibex (*.ibex), a file preprocessed using in the analysis/ directory, and a file filtered using filter.R in the analysis/ directory.


This directory contains preprocessing scripts (, filter.R), implementations of the optimizers for the ordinal models (, and implementations of the class ( and similarity prediction ( models.


This directory contains the two papers mentioned in the overview.