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 advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback

Multi-argument classification for semantic role labeling

This paper describes a Multi-Argument Classification (MAC) approach to Semantic Role Labeling. The goal is to exploit dependencies between semantic roles by simultaneously classifying all arguments as a pattern. Argument identification, as a pre-processing stage, is carried at using the improved Predicate-Argument Recognition Algorithm (PARA) developed by Lin and Smith (2006). Results using standard evaluation metrics show that multi-argument classification, archieving 76.60 in F₁ measurement on WSJ 23, outperforms existing systems that use a single parse tree for the CoNLL 2005 shared task data. This paper also describes ways to significantly increase the speed of multi-argument classification, making it suitable for real-time language processing tasks that require semantic role labelling