Computational Linguistics Colloquium

Thursday, 12 November, 16:15
Conference Room, Building C7 4

Current Trends in Data-Driven Dependency Parsing

Dependency-based syntactic parsing has become a standard technique in natural language processing and a number of different models have been proposed in recent years, in particular data-driven models that can be trained using syntactically annotated corpora, or treebanks. Most of these models can be characterized as either graph-based or transition-based. Graph-based models learn to score entire dependency trees and use exact search to find the best tree for a given input sentence. Transition-based models learn to score local parsing actions and use greedy search to find the best sequence of actions for a given input sentence. Both types of models give state-of-the-art accuracy but a comparative error analysis reveals that they have different error distributions and that this difference can be tied to theoretical properties of the models. Recent work on data-driven parsing has therefore to a large extent been characterized by attempts to combine the strengths of the two models, either through the development of hybrid models or through system combination. In this talk, I review the classic graph-based and transition-based models, characterize their typical strengths and weaknesses, and report on recent work aiming to improve the basic models.

If you would like to meet with the speaker, please contact Yi Zhang.