---
title: "A Breadth-First Parsing Model"
authors:
  - "John Bear"
doi: ""
url: "https://ijcai.org/proceedings/1983/168"
pdf: "https://ijcai.org/proceedings/1983/0168.pdf"
venue: "Proceedings of the Eighth International Joint Conference on Artificial Intelligence"
year: 1983
pages: "696-697"
track: "Natural Language"
keywords:
---

# A Breadth-First Parsing Model

John Bear

*Proceedings of the Eighth International Joint Conference on Artificial Intelligence*, pages 696-697.

## Abstract

Recent attempts at modeling humans' abilitiesat processing natural language have centered around depth first parsing algorithms, and control strategies for making the best choices for disambiguation and attachment. This paper proposes a breadth-first algor ithrn as a model. The algorithm avoids sane of the carrnon pitfalls of depth-first approaches regarding ambiguity, and by using more pre-canputed information about the grammar, avoids sane of the usual proolans of parallel parsing algorithms as well.