Script Knowledge for Modelling Semantic Expectation
Project A2 Completed
Project A2 is concerned with the development of wide-coverage, automatic methods for acquiring script knowledge, thus addressing the absence of such script knowledge bases. Since script event sequences are rarely explicit in natural prose, the project will use crowd-sourcing methods to create suitable corpora for script acquisition. These will then serve as the input for novel script-mining algorithms which will be used to induce psychologically plausible probabilistic script-automata representations.
Finally, distributional models will be applied to determine the semantic similarity of linguistic expressions, as conditioned by script knowledge – methods that are essential for applying scripts to real texts. The script resources created in this project will inform the development of experimental stimuli in A1, and will be used and evaluated directly in the models developed in A3.
Keywords: computational linguistics, crowdsourcing, script knowledge, world knowledge, script mining, distributional models
Other Area-A Projects
- Neurobehavioural Correlates of Surprisal in Online Comprehension A1
- Modelling the Information Density of Event Sequences in Texts A3
- Language Comprehension in a Noisy Channel A4
- The Role of Language Experience and Visual Context in Surprisal A5
- The Role of Semantic Surprisal for Memory Formation and Retrieval A6
- Controlling Information Density in Discourse Generation A7