This paper explores Chinese semantic role labeling (SRL) for nominal predicates. Besides those widely used features in verbal SRL, various nominal SRL-specific features are first included. Then, we improve the performance of nominal SRL by integrating useful features derived from a state-of-the-art verbal SRL system. Finally, we address the issue of automatic predicate recognition, which is ess...

In this paper we present a semantic role labeling system submitted to the task Multilevel Semantic Annotation of Catalan and Spanish in the context of SemEval–2007. The core of the system is a memory–based classifier that makes use of full syntactic information. Building on standard features, we train two classifiers to predict separately the semantic class of the verb and the semantic roles.

In this paper, we evaluate a semantic role labeling approach to the extraction of answers in the open domain question answering task. We show that this technique especially improves the system performance when answers are communicated to the user by voice. Semantic role labeling identifies predicates and semantic argument phrases in a sentence. With this information we are able to analyze and e...

This paper reports an approach to automatically generate a lexical resource to support incremental semantic role labeling annotation in Portuguese. The data come from the corpus Propbank-Br (Propbank of Brazilian Portuguese) and from the lexical resource of English Propbank, as both share the same structure. In order to enable the strategy, we added extra annotation to Propbank-Br. This approac...

We present a four-step hierarchical SRL strategy which generalizes the classical two-level approach (boundary detection and classification). To achieve this, we have split the classification step by grouping together roles which share linguistic properties (e.g. Core Roles versus Adjuncts). The results show that the nonoptimized hierarchical approach is computationally more efficient than the t...

State-of-the-art semantic role labelling systems require large annotated corpora to achieve full performance. Unfortunately, such corpora are expensive to produce and often do not generalize well across domains. Even in domain, errors are often made where syntactic information does not provide sufficient cues. In this paper, we mitigate both of these problems by employing distributional word re...