Speaker
Affilliation
Uppsala University, Sweden
Abstract
The first part of the talk introduces the transition-based approach to data-driven dependency parsing, where inference is performed as a greedy best-first search over a non-deterministic transition system, while learning is reduced to the simple classification problem of mapping each parser state to the correct transition out of that state. The second part of the talk explores the idea that non-projective dependency parsing can be conceived as the outcome of two interleaved processes, one that sorts the words of a sentence into a canonical order, and one that performs strictly projective dependency parsing on the sorted input. Based on this idea, a parsing algorithm is constructed by combining an online sorting algorithm with a transition system for projective dependency parsing.