A breadth-first parsing model

Authors:
John Bear
Affiliations:
Linguistics Research Center, University of Texas, Austin, Texas
Venue:
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Year:
1983

Citing 3
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Abstract

Recent attempts at modeling humans' abilities at 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 algorithm as a model. The algorithm avoids some of the common pitfalls of depth-first approaches regarding ambiguity, and by using more pre-ccmputed information about the grammar, avoids same of the usual problems of parallel parsing algorithms as well.