Abstract
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(n3). The parser is publicly available.1