Phase-based Minimalist Parsing and complexity in non-local dependencies
This work addresses cognitive plausibility in parsing algorithms for linguists and cognitive scientists, though it is incremental as it builds on existing frameworks.
The paper tackles the problem of modeling human parsing complexity in non-local dependencies by proposing an adaptation of Earley's algorithm for Phase-based Minimalist Grammars, which predicts complexity effects that fit reading time data from self-paced experiments on object clefts sentences.
A cognitively plausible parsing algorithm should perform like the human parser in critical contexts. Here I propose an adaptation of Earley's parsing algorithm, suitable for Phase-based Minimalist Grammars (PMG, Chesi 2012), that is able to predict complexity effects in performance. Focusing on self-paced reading experiments of object clefts sentences (Warren & Gibson 2005) I will associate to parsing a complexity metric based on cued features to be retrieved at the verb segment (Feature Retrieval & Encoding Cost, FREC). FREC is crucially based on the usage of memory predicted by the discussed parsing algorithm and it correctly fits with the reading time revealed.