UCLA

Psycholinguistic research has posited arc-eager left corner parsing as a psychologically viable candidate for the human parsing mechanism (Resnik, 1992). Using probabilistic left-corner grammars (PLCGs), as introduced by Manning and Carpenter (1997), as a testbed, this thesis examines the probabilistic mechanisms involved in arc-eager tree construction. By moving attachment decisions earlier in the decision tree, arc-eager PLCGs gain probabilistic advantage over their arc-standard counterparts due to recursive left-corner embeddings, tree productions of the form A -> Aγ, which are abundant in datasets like the Penn Treebank, and which many would argue have psychological reality. This advantage is fully independent of the well-documented stack management advantage seen in arc-eager constructions of right-branching structures. The python module ae-plcg, which was created and used by the present investigation to model and evaluate arc-standard and arc-eager PLCGs, can be found at https://github.com/phill-barnett/ae-plcg, along with several fully trained and testable model parameter sets, detailed in Section 3.1 of this thesis.