# Phase-based Minimalist Parsing and complexity in non-local dependencies

## TL;DR

This paper introduces a phase-based minimalist parsing algorithm adapted from Earley's method, capable of predicting complexity effects in human sentence processing, validated through reading time experiments on object cleft sentences.

## Contribution

It presents a novel adaptation of Earley's algorithm for Phase-based Minimalist Grammars that predicts cognitive complexity effects in sentence parsing.

## Key findings

- FREC metric correlates with reading times in experiments
- The parsing algorithm predicts human-like processing complexity
- Memory usage in parsing aligns with observed reading difficulty

## Abstract

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.

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Source: https://tomesphere.com/paper/1906.00908