Paper: Semantic Role Labelling With Tree Conditional Random Fields

ACL ID W05-0622
Title Semantic Role Labelling With Tree Conditional Random Fields
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

In this paper we apply conditional random fields (CRFs) to the semantic role labelling task. We define a random field over the structure of each sentence’s syntactic parse tree. For each node of the tree, the model must predict a semantic role label, which is interpreted as the labelling for the corresponding syntactic constituent. We show how modelling the task as a tree labelling problem allows for the use of efficient CRF inference algorithms, while also increasing generalisation performance when compared to the equivalent maximum entropy classifier. We have participated in the CoNLL-2005 shared task closed challenge with full syntactic information.