Our NER system achieves the best current result on the widely used CoNLL benchmark.

Our system achieved the best current result on the CoNLL NER data set.

Our named entity recognition system achieves an Fl-score of 90.90 on the CoNLL 2003 English data set, which is about 1 point higher than the previous best result.

The CoNLL 2003 Shared Task (Tjong Kim Sang and Meulder 2003) offered a standard experimental platform for NER.

The CoNLL data set consists of news articles from Reutersl.

We adopted the same evaluation criteria as the CoNLL 2003 Shared Task.

For these rules, we refer to the conversion of the Penn Treebank into dependency structures used in the CoNLL 2008 Shared Task, and mark the heads of these rules in a way that will arrive at a compatible dependency backbone.

In combination with the right-branching analysis of coordination in ERG, this leads to the same dependency attachment in the CoNLL syntax.

the CoNLL shared task dependency structures, minor systematic differences still exist for some phenomena.

To evaluate the performance of our different dependency parsing models, we tested our approaches on several dependency treebanks for English in a similar spirit to the CoNLL 2006-2008 Shared Tasks.

In previous years of CoNLL Shared Tasks, several datasets have been created for the purpose of dependency parser evaluation.

Our experiments adhere to the CoNLL 2008 dependency syntax (Yamada et al.

In the meantime, successful continuation of CoNLL Shared Tasks since 2006 (Buchholz and Marsi, 2006; Nivre et al., 2007a; Surdeanu et al., 2008) have witnessed how easy it has become to train a statistical syntactic dependency parser provided that there is annotated treebank.

In recent years, two statistical dependency parsing systems, MaltParser (Nivre et al., 2007b) and MS TParser (McDonald et al., 2005b), representing different threads of research in data-driven machine learning approaches have obtained high publicity, for their state-of-the-art performances in open competitions such as CoNLL Shared Tasks.

Following the CoNLL shared task from 2000, we use sections 15-18 of the Penn Treebank for our labeled training data for the supervised sequence labeler in all experiments (Tjong et al., 2000).

We tested the accuracy of our models for chunking and POS tagging on section 20 of the Penn Treebank, which corresponds to the test set from the CoNLL 2000 task.

The chunker’s accuracy is roughly in the middle of the range of results for the original CoNLL 2000 shared task (Tjong et al., 2000) .

Ando and Zhang develop a semi-supervised chunker that outperforms purely supervised approaches on the CoNLL 2000 dataset (Ando and Zhang, 2005).

We evaluate our approach on Bulgarian and Spanish CoNLL shared task data and show that we consistently outperform unsupervised methods and can outperform supervised learning for limited training data.

gtreebank corpus from CoNLL X.

Figure 2: Learning curve of the discriminative no-rules transfer model on Bulgarian bitext, testing on CoNLL train sentences of up to 10 words.

We evaluate our results on the Bulgarian and Spanish corpora from the CoNLL X shared task.

PB is a standard corpus for SRL evaluation and was used in the CoNLL SRL shared tasks of 2004 (Carreras and Marquez, 2004) and 2005 (Carreras and Marquez, 2005).

The CoNLL shared tasks of 2004 and 2005 were devoted to SRL, and studied the influence of different syntactic annotations and domain changes on SRL results.

Supervised clause detection was also tackled as a separate task, notably in the CoNLL 2001 shared task (Tjong Kim Sang and Dejean, 2001).

The first set of experiments used the text chunking data set provided for the CoNLL 2000 shared task.5 The training data consists of 8,936 sentences in which each token is annotated with the “ICE” tags representing text chunks such as noun and verb phrases.

Figure 3: CoNLL 2000 chunking task: Objective

Figure 4: CoNLL 2000 chunking task: Number of active features.