Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful −they all outperform the current best results reported in the CoNLL-2005 evaluation exercise− but each of the proposed approaches has its a...

• Type: Journal
• Year: 2007
• Where: JAIR
• Authors: Mihai Surdeanu, Lluís Màrquez, Xavier Carreras, Pere Comas
• Keywords: Inference, Inference Model, Inference Strategies, JAIR 2007