In this paper we describe the CoNLL2005 shared task on Semantic Role Labeling.

In this paper we describe the CoNLL-2004 shared task: semantic role labeling. We introduce

Abstract not found

Most information extraction (IE) systems treat separate potential extractions as independent. However, in many cases, considering inuences between dierent potential extractions could improve overall accuracy. Statistical methods based on undirected graphical models, such as conditional random elds (CRFs), have been shown to be an eective approach to learning accurate IE systems. We present a new IE method that employs Relational Markov Networks (a generalization of CRFs), which can represent arbitrary dependencies between extractions. This allows for \collective information extraction" that exploits the mutual in- uence between possible extractions. Experiments on learning to extract protein names from biomedical text demonstrate the advantages of this approach.

A hybrid convolution tree kernel is proposed in this paper to effectively model syntactic structures for semantic role labeling (SRL). The hybrid kernel consists of two individual convolution kernels: a Path kernel, which captures predicateargument link features, and a Constituent Structure kernel, which captures the syntactic structure features of arguments. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the novel hybrid convolution tree kernel outperforms the previous tree kernels. We also combine our new hybrid tree kernel based method with the standard rich flat feature based method. The experimental results show that the combinational method can get better performance than each of them individually. 1

Discriminative probabilistic models are very popular in NLP because of the latitude they afford in designing features. But training involves complex trade-offs among weights, which can be dangerous: a few highlyindicative features can swamp the contribution of many individually weaker features, causing their weights to be undertrained. Such a model is less robust, for the highly-indicative features may be noisy or missing in the test data. To ameliorate this weight undertraining, we introduce several new feature bagging methods, in which separate models are trained on subsets of the original features, and combined using a mixture model or a product of experts. These methods include the logarithmic opinion pools used by Smith et al. (2005). We evaluate feature bagging on linear-chain conditional random fields for two natural-language tasks. On both tasks, the feature-bagged CRF performs better than simply training a single CRF on all the features. 1

Discriminatively-trained probabilistic models are very popular in NLP because of the latitude they afford in designing features. But training involves complex trade-offs among weights, which can be dangerous: a few highlyindicative features can swamp the contribution of many individually weaker features, causing their weights to be undertrained. Such a model is less robust, for the highly-indicative features may be noisy or missing in the test data. To ameliorate this weight undertraining, we propose a new training method, called feature bagging, in which separate models are trained on subsets of the original features, and combined using a mixture model or a product of experts. We evaluate feature bagging on linear-chain conditional random fields for two natural-language tasks. On both tasks, the feature-bagged CRF performs better than simply training a single CRF on all the features. 1

We present a simple history-based model for sentence generation from LFG f-structures, which improves on the accuracy of previous models by breaking down PCFG independence assumptions so that more f-structure conditioning context is used in the prediction of grammar rule expansions. In addition, we present work on experiments with named entities and other multi-word units, showing a statistically significant improvement of generation accuracy. Tested on section 23 of the Penn Wall Street Journal Treebank, the techniques described in this paper improve BLEU scores from 66.52 to 68.82, and coverage from 98.18 % to 99.96%. 1

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This paper presents the results of applying transformation-based learning (TBL) to the problem of semantic role labeling. The great advantage of the TBL paradigm is that it provides a simple learning framework in which the parallel tasks of argument identification and argument labeling can mutually influence one another. Semantic role