We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role labeling. We show that full syntactic parsing information is, by far, most relevant in identifying the argument, especially, in the very first stage—the pruning stage. Surprisingly, the quality of the pruning stage cannot be solely determined based on its recall and precision. Instead, it depends on the characteristics of the output candidates that determine the difficulty of the downstream problems. Motivated by this observation, we propose an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves its performance. Our system has been evaluated in the CoNLL-2005 shared task on semantic role labeling, and achieves the highest F1 score among 19 participants.

We present a system for the semantic role labeling task. The system combines a machine learning technique with an inference procedure based on integer linear programming that supports the incorporation of linguistic and structural constraints into the decision process. The system is tested on the data provided in the CoNLL-2004 shared task on semantic role labeling and achieves very competitive results.

We present an approach to semantic role labeling (SRL) that takes the output of multiple argument classifiers and combines them into a coherent predicateargument output by solving an optimization problem. The optimization stage, which is solved via integer linear programming, takes into account both the recommendation of the classifiers and a set of problem specific constraints, and is thus used both to clean the classification results and to ensure structural integrity of the final role labeling. We illustrate a significant improvement in overall SRL performance through this inference. SRL System ArchitectureOur SRL system consists of four stages: pruning, argument identification, argument classification, and inference. In particular, the goal of pruning and argument identification is to identify argument candidates for a given verb predicate. The system only classifies the argument candidates into their types during the argument classification stage. Linguistic and structural constraints are incorporated in the inference stage to resolve inconsistent global predictions. The inference stage can take as its input the output of the argument classification of a single system or of multiple systems. We explain the inference for multiple systems in Sec. 2. PruningOnly the constituents in the parse tree are considered as argument candidates. In addition, our system exploits the heuristic introduced by (Xue and Palmer, 2004) to filter out very unlikely constituents. The heuristic is a recursive process starting from the verb whose arguments are to be identified. It first returns the siblings of the verb; then it moves to the parent of the verb, and collects the siblings again. The process goes on until it reaches the root. In addition, if a constituent is a PP (propositional phrase), its children are also collected. Candidates consisting of only a single punctuation mark are not considered.This heuristic works well with the correct parse trees. However, one of the errors by automatic parsers is due to incorrect PP attachment leading to missing arguments. To attempt to fix this, we consider as arguments the combination of any consecutive NP and PP, and the split of NP and PP inside the NP that was chosen by the previous heuristics. Argument IdentificationThe argument identification stage utilizes binary classification to identify whether a candidate is an argument or not. We train and apply the binary classifiers on the constituents supplied by the pruning stage. Most of the features used in our system are standard features, which include• Predicate and POS tag of predicate indicate the lemma of the predicate and its POS tag.• Voice indicates tbe voice of the predicate.• Phrase type of the constituent.• Head word and POS tag of the head word include head word and its POS tag of the constituent. We use rules introduced by (Collins, 1999) to extract this feature.• First and last words and POS tags of the constituent.• Two POS tags before and after the constituent.• Position feature describes if the constituent is...

We present a system submitted to the CoNLL-2004 shared task for semantic role labeling. The system is composed of a set of classifiers and an inference procedure used both to clean the classification results and to ensure structural integrity of the final role labeling. Linguistic information is used to generate features during classification and constraints for the inference process.