Semantic role labeling techniques are typically trained on newswire text, and in tests their performance on fiction is as much as 19% worse than their performance on newswire text.

We investigate techniques for building open-domain semantic role labeling systems that approach the ideal of a train-once, use-anywhere system.

We leverage recently-developed techniques for learning representations of text using latent-variable language models, and extend these techniques to ones that provide the kinds of features that are useful for semantic role labeling .

In recent semantic role labeling (SRL) competitions such as the shared tasks of CoNLL 2005 and CoNLL 2008, supervised SRL systems have been trained on newswire text, and then tested on both an in-domain test set (Wall Street Journal text) and an out-of-domain test set (fiction).

We test our open-domain semantic role labeling system using data from the CoNLL 2005 shared task (Carreras and Marquez, 2005).

Owing to the established difficulty of the Brown test set and the different domains of the Brown test and WSJ training data, this dataset makes for an excellent testbed for open-domain semantic role labeling .

In this paper we focus on the parsing and argument-identification steps that precede Semantic Role Labeling (SRL) training.

The results show that proposed shallow representations of sentence structure are robust to reductions in parsing accuracy, and that the contribution of alternative representations of sentence structure to successful semantic role labeling varies with the integrity of the parsing and argument-identification stages.

have the luxury of treating part-of-speech tagging and semantic role labeling as separable tasks.

In this paper we present experiments with an automatic system for semantic role labeling (SRL) that is designed to model aspects of human language acquisition.

n Semantic Role Labeling

Previous computational experiments with a system for automatic semantic role labeling (BabySRL: (Connor et al., 2008)) showed that it is possible to learn to assign basic semantic roles based on the shallow sentence representations proposed by the structure-mapping view.

We model language learning as a Semantic Role Labeling (SRL) task (Carreras and Marquez, 2004).

Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples.

The aim of the evaluation is to measure the reachable accuracy of the simple model proposed and to compare its impact over in-domain and out-of-domain semantic role labeling tasks.

Semantic Role Labeling (SRL) is the task of automatic recognition of individual predicates together with their major roles (e.g.

More recently, the state-of-art frame-based semantic role labeling system discussed in (Johansson and Nugues, 2008b) reports a 19% drop in accuracy for the argument classification task when a different test domain is targeted (i.e.

The task of distinguishing between the two has strong relations to various basic NLP tasks such as syntactic parsing, semantic role labeling and subcategorization acquisition.

Semantic Role Labeling .

Distinguishing between the two argument types has been discussed extensively in various formulations in the NLP literature, notably in PP attachment, semantic role labeling (SRL) and subcategorization acquisition.