This paper focuses on semantic role labeling using automatically-generated syntactic information. A simple and robust strategy for system combination is presented, which allows to partially recover from input parsing errors and to significantly boost results of individual systems. This combination scheme is also very flexible since the individual systems are not required to provide any informat...

Semantic labeling is a transformation technique for proving the termination of rewrite systems. The semantic part is given by a quasi-model of the rewrite rules. In this paper we present a variant of semantic labeling in which the quasi-model condition is only demanded for the usable rules induced by the labeling. Our variant is less powerful in theory but maybe more useful in practice.

We present a comparison between two systems for establishing syntactic and semantic dependencies: one that performs dependency parsing and semantic role labeling as a single task, and another that performs the two tasks in isolation. The systems are based on local memorybased classifiers predicting syntactic and semantic dependency relations between pairs of words. In a second global phase, the...

In this paper we describe the CoNLL-2004 shared task: semantic role labeling. We introduce the specification and goal of the task, describe the data sets and evaluation methods, and present a general overview of the systems that have contributed to the task, providing comparative description.

In this paper we describe the CoNLL2005 shared task on Semantic Role Labeling. We introduce the specification and goals of the task, describe the data sets and evaluation methods, and present a general overview of the 19 systems that have contributed to the task, providing a comparative description and results.

We propose a novel approach to crosslingual model transfer based on feature representation projection. First, a compact feature representation relevant for the task in question is constructed for either language independently and then the mapping between the two representations is determined using parallel data. The target instance can then be mapped into the source-side feature representation ...

This paper describes our experience in preparing the data and evaluating the results for three subtasks of SemEval-2007 Task-17 – Lexical Sample, Semantic Role Labeling (SRL) and All-Words respectively. We tabulate and analyze the results of participating systems.

We propose a system to carry out the joint parsing of syntactic and semantic dependencies in multiple languages for our participation in the shared task of CoNLL-2009. We present an iterative approach for dependency parsing and semantic role labeling. We have participated in the closed challenge, and our system achieves 73.98% on labeled macro F1 for the complete problem, 77.11% on labeled atta...

This paper addresses the task of finding antecedents for locally uninstantiated arguments. To resolve such null instantiations, we develop a weakly supervised approach that investigates and combines a number of linguistically motivated strategies that are inspired by work on semantic role labeling and corefence resolution. The performance of the system is competitive with the current state-of-t...

We describe our CoNLL 2009 Shared Task system in the present paper. The system includes three cascaded components: a generative dependency parser, a classifier for syntactic dependency labels and a semantic classifier. The experimental results show that the labeled macro F1 scores of our system on the joint task range from 43.50% (Chinese) to 57.95% (Czech), with an average of 51.07%.