An Exploration of Left-Corner Transformations
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Author / Producer
Date
2023-12
Publication Type
Conference Paper
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yes
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Abstract
The left-corner transformation (Rosenkrantz and Lewis, 1970) is used to remove left recursion from context-free grammars, which is an important step towards making the grammar parsable top-down with simple techniques. This paper generalizes prior left-corner transformations to support semiring-weighted production rules and to provide finer-grained control over which left corners may be moved. Our generalized left-corner transformation (GLCT) arose from unifying the left-corner transformation and speculation transformation (Eisner and Blatz, 2007), originally for logic programming. Our new transformation and speculation define equivalent weighted languages. Yet, their derivation trees are structurally different in an important way: GLCT replaces left recursion with right recursion, and speculation does not. We also provide several technical results regarding the formal relationships between the outputs of GLCT, speculation, and the original grammar. Lastly, we empirically investigate the efficiency of GLCT for left-recursion elimination from grammars of nine languages. Code: https://github.com/rycolab/left-corner
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Publication status
published
Book title
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Journal / series
Volume
Pages / Article No.
13393 - 13427
Publisher
Association for Computational Linguistics
Event
2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09462 - Hofmann, Thomas / Hofmann, Thomas
09682 - Cotterell, Ryan / Cotterell, Ryan
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Funding
Related publications and datasets
Is supplemented by: https://github.com/rycolab/left-corner