Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky's (1995) Minimalist Program.This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing.The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014;Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees.We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies.Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is O(n 3 ).The parser is publicly available.

This paper reports on two strategies that have been implemented for improving the efficiency and precision of wide-coverage Minimalist Grammar (MG) parsing.The first extends the formalism presented in Torr and Stabler (2016) with a mechanism for enforcing fine-grained selectional restrictions and agreements.The second is a method for factoring computationally costly null heads out from bottom-up MG parsing; this has the additional benefit of rendering the formalism fully compatible for the first time with highly efficient Markovian supertaggers.These techniques aided in the task of generating MGbank, the first wide-coverage corpus of Minimalist Grammar derivation trees.