Minimalist Grammar Transition-Based Parsing

Abstract

Current chart-based parsers of Minimalist Grammars exhibit prohibitively high polynomial complexity that makes them unusable in practice. This paper presents a transition-based parser for Minimalist Grammars that approximately searches through the space of possible derivations by means of beam search, and does so very efficiently: the worst case complexity of building one derivation is $$O(n^2)$$ O ( n 2 ) and the best case complexity is O(n). This approximated inference can be guided by a trained probabilistic model that can condition on larger context than standard chart-based parsers. The transitions of the parser are very similar to the transitions of bottom-up shift-reduce parsers for Context-Free Grammars, with additional transitions for online reordering of words during parsing in order to make non-projective derivations projective.

Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing

“…- Stanojević (2017) shows how bottom-up transition-based parsers can be provided for MGs, and those allow LSTMs and other neural systems to be trained as oracles (Lewis et al, 2016). It would be interesting to explore similar oracles for slightly more predictive methods like LC, and trained on recently built MGbank (Torr, 2018).…”

Robert C. Berwick and Edward P. Stabler, editors. Minimalist Parsing. Oxford: Oxford University Press, 2019. ISBN: 978-0-19-879508-7. Price. $40.00 (paperback). $84.00 (hardcover). xvi+192 pages

“…As early as in 1992, he published a monograph on the formalization and computational implementation of Government and Binding version of generative grammar. His seminal paper of 1997 laid the foundation for the MG, which forms the backbone of this book, and gave rise to a large amount of research, including doctoral theses supervised by Stabler himself (such as Harkema 2001;Kobele 2006), and a number of papers presented at the International Conference on Logical Aspects of Computational Linguistics, one of the top conferences in the field (extending from Cornell 1999to Stanojević 2016. This research has progressed so far that the properties of MG, such as its generative power and the complexity of parsing them, are already well studied (p. 12).…”

On the Computational Complexity of Head Movement and Affix Hopping

“…The published work on Minimalist Parsing can be divided into two categories. The first is transition based parsing which is usually of higher relevance to psycho-linguists as a more likely model of human sentence processing [8,9,12,13,17,[30][31][32][33]. However, these models use no dynamic programming and therefore have exponential runtime complexity.…”