A Fundamental Algorithm for Dependency Parsing (With Corrections)

TL;DR

This paper introduces a fundamental, brain-inspired dependency parsing algorithm that processes sentences incrementally, offering a new approach with comparable complexity to traditional methods but potentially better alignment with human language processing.

Contribution

The paper proposes a novel incremental dependency parsing algorithm that operates one word at a time, differing from traditional phrase-structure parsers and aligning with human cognitive processes.

Findings

Operates incrementally, attaching words as soon as possible.

Complexity is $O(n^3)$ in worst case, similar to existing parsers.

Worst-case scenarios are rare in typical language use.

Abstract

This paper presents a fundamental algorithm for parsing natural language sentences into dependency trees. Unlike phrase-structure (constituency) parsers, this algorithm operates one word at a time, attaching each word as soon as it can be attached, corresponding to properties claimed for the parser in the human brain. Like phrase-structure parsing, its worst-case complexity is $O(n^3)$, but in human language, the worst case occurs only for small $n$.